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1307
Navigation; Safety; Tracking; Autonomous vehicles; pedestrian detection
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Advanced driver assistance system (ADAS)
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1302
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A cheaper way for robocars to avoid pedestrians,
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Nakagawa, C.,
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Ito, T.,
Evaluation of the Effects of a Personal Mobility Vehicle on Multiple
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1508
Indexes
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IET-ITS(10), No. 6, 2016, pp. 438-444.
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IET-IPR(11), No. 10, October 2017, pp. 833-840.
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Early Detection of Sudden Pedestrian Crossing for Safe Driving During
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CirSysVideo(27), No. 6, June 2017, pp. 1368-1380.
IEEE DOI
1706
Cameras, Feature extraction, Finite impulse response filters,
Image color analysis, Roads, Support vector machines, Vehicles,
Cascade random forest (CaRF),
Keimyung University (KMU) pedestrian data set,
far-infrared (FIR) image, sudden pedestrian crossing (SPC),
virtual reference line
BibRef
Rosado, A.L.[A. López],
Chien, S.,
Li, L.,
Yi, Q.,
Chen, Y.,
Sherony, R.,
Certainty and Critical Speed for Decision Making in Tests of
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ITS(18), No. 6, June 2017, pp. 1358-1370.
IEEE DOI
1706
Analytical models, Automobiles, Computer crashes, Decision making,
Safety, Vehicle crash testing, Pedestrian protection,
active safety margin, critical speed for decision making,
prediction, model
BibRef
Doric, I.,
Reitberger, A.,
Wittmann, S.,
Harrison, R.,
Brandmeier, T.,
A Novel Approach for the Test of Active Pedestrian Safety Systems,
ITS(18), No. 5, May 2017, pp. 1299-1312.
IEEE DOI
1705
Accidents, Knee, Legged locomotion, Microscopy, Roads, Safety,
Sensor systems, ADAS, pedestrian detection,
BibRef
Tang, S.[Suhua],
Obana, S.[Sadao],
Improving performance of pedestrian positioning
by using vehicular communication signals,
IET-ITS(12), No. 5, June 2018, pp. 366-374.
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1805
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Rajkumar, R.,
Tools and Methodologies for Autonomous Driving Systems,
PIEEE(106), No. 9, September 2018, pp. 1700-1716.
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1810
automobiles, embedded systems, mobile robots, pedestrians,
road safety, safety-critical software, verification,
software tools
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Campbell, B.S.,
May, D.C.,
Bethel, C.L.,
Usher, J.M.,
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Understanding Human Response to the Presence and Actions of Unmanned
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HMS(48), No. 4, August 2018, pp. 325-336.
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1808
human-robot interaction, mobile robots, pedestrians,
remotely operated vehicles, sport, video signal processing,
remotely operated vehicles
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Deb, S.,
Rahman, M.M.,
Strawderman, L.J.,
Garrison, T.M.,
Pedestrians' Receptivity Toward Fully Automated Vehicles:
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HMS(48), No. 3, June 2018, pp. 279-290.
IEEE DOI
1805
More the reaction to no driver.
Automation, Automobiles, Legged locomotion, Roads,
Vehicle crash testing, Autonomous vehicles,
virtual reality (VR)
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Flores, C.,
Merdrignac, P.,
de Charette, R.,
Navas, F.,
Milanés, V.,
Nashashibi, F.,
A Cooperative Car-Following/Emergency Braking System With
Prediction-Based Pedestrian Avoidance Capabilities,
ITS(20), No. 5, May 2019, pp. 1837-1846.
IEEE DOI
1905
Laser radar, Global Positioning System, Kalman filters,
Image segmentation, Urban areas, Safety, Cooperative systems,
collision avoidance system
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Castaldo, F.[Francesco],
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Long-term path prediction in urban scenarios using circular
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IVC(69), 2018, pp. 81-91.
Elsevier DOI
1802
Predict near-future for pedestrians, etc.
Long-term path prediction, Circular distribution,
Human-scene interaction, Stochastic model
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Deb, S.,
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How Communicating Features can Help Pedestrian Safety in the Presence
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IEEE DOI
2004
Autonomous vehicles (AVs), communicating features,
human-automation interaction, pedestrian safety, virtual reality (VR)
BibRef
Goldhammer, M.,
Köhler, S.,
Zernetsch, S.,
Doll, K.,
Sick, B.,
Dietmayer, K.,
Intentions of Vulnerable Road Users: Detection and Forecasting by
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ITS(21), No. 7, July 2020, pp. 3035-3045.
IEEE DOI
2007
Trajectory, Predictive models, Hidden Markov models, Roads, Cameras,
Machine learning, Time series analysis, Road safety,
artificial neural networks
BibRef
Yu, B.,
Zhu, K.,
Wu, K.,
Zhang, M.,
Improved OpenCL-Based Implementation of Social Field Pedestrian Model,
ITS(21), No. 7, July 2020, pp. 2828-2839.
IEEE DOI
2007
Computational modeling, Graphics processing units, Force,
Numerical models, Legged locomotion,
pedestrian flow
BibRef
Liberto, C.[Carlo],
Nigro, M.[Marialisa],
Carrese, S.[Stefano],
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Simulation framework for pedestrian dynamics: modelling and calibration,
IET-ITS(14), No. 9, September 2020, pp. 1048-1057.
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2008
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Zhou, Z.P.[Zhu-Ping],
Peng, Y.L.[Yun-Long],
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Vision-based approach for predicting the probability of
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IET-ITS(14), No. 11, November 2020, pp. 1447-1455.
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2010
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Elhenawy, M.[Mohammed],
Ashqar, H.I.[Huthaifa I.],
Masoud, M.[Mahmoud],
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Rakotonirainy, A.[Andry],
Rakha, H.A.[Hesham A.],
Deep Transfer Learning for Vulnerable Road Users Detection using
Smartphone Sensors Data,
RS(12), No. 21, 2020, pp. xx-yy.
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2011
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Hou, L.,
Xin, L.,
Li, S.E.,
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Wang, W.,
Interactive Trajectory Prediction of Surrounding Road Users for
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ITS(21), No. 11, November 2020, pp. 4615-4625.
IEEE DOI
2011
Trajectory, Roads, Predictive models, Prototypes,
Computational modeling, Decoding, Autonomous vehicles,
LSTM
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Rasouli, A.,
Tsotsos, J.K.,
Autonomous Vehicles That Interact With Pedestrians:
A Survey of Theory and Practice,
ITS(21), No. 3, March 2020, pp. 900-918.
IEEE DOI
2003
Survey, Pedestrian Detection. Autonomous vehicles, Roads, Cameras, Automobiles, Observers,
pedestrian behavior, traffic interaction, survey
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Jones, E.[Edward],
Glavin, M.[Martin],
Overcoming Occlusion in the Automotive Environment: A Review,
ITS(22), No. 1, January 2021, pp. 23-35.
IEEE DOI
2012
Automotive engineering, Object recognition, Object detection,
Roads, Cognition, Task analysis, Automation, Occlusion handling,
autonomous vehicles
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Pugh, N.[Nigel],
Park, H.[Hyoshin],
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Deep adaptive learning for safe and efficient navigation of
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IET-ITS(15), No. 4, 2021, pp. 538-548.
DOI Link
2106
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Yu, K.P.[Ke-Ping],
Lin, L.[Long],
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Deep Learning-Based Traffic Safety Solution for a Mixture of
Autonomous and Manual Vehicles in a 5G-Enabled Intelligent
Transportation System,
ITS(22), No. 7, July 2021, pp. 4337-4347.
IEEE DOI
2107
Vehicles, Hidden Markov models, Safety, Manuals,
5G mobile communication, Real-time systems, Autonomous vehicles,
intention recognition
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Camara, F.[Fanta],
Bellotto, N.[Nicola],
Cosar, S.[Serhan],
Nathanael, D.[Dimitris],
Althoff, M.[Matthias],
Wu, J.Y.[Jing-Yuan],
Ruenz, J.[Johannes],
Dietrich, A.[André],
Fox, C.W.[Charles W.],
Pedestrian Models for Autonomous Driving Part I:
Low-Level Models, From Sensing to Tracking,
ITS(22), No. 10, October 2021, pp. 6131-6151.
IEEE DOI
2110
BibRef
And:
Erratum:
ITS(22), No. 11, November 2021, pp. 7317-7317.
IEEE DOI
2112
Sensors, Cameras, Psychology, Autonomous vehicles, Predictive models,
Computational modeling, Review, survey, pedestrians,
datasets
BibRef
Camara, F.[Fanta],
Bellotto, N.[Nicola],
Cosar, S.[Serhan],
Weber, F.[Florian],
Nathanael, D.[Dimitris],
Althoff, M.[Matthias],
Wu, J.Y.[Jing-Yuan],
Ruenz, J.[Johannes],
Dietrich, A.[André],
Markkula, G.[Gustav],
Schieben, A.[Anna],
Tango, F.[Fabio],
Merat, N.[Natasha],
Fox, C.[Charles],
Pedestrian Models for Autonomous Driving Part II: High-Level Models
of Human Behavior,
ITS(22), No. 9, September 2021, pp. 5453-5472.
IEEE DOI
2109
BibRef
And:
Erratum:
ITS(22), No. 11, November 2021, pp. 7317-7317.
IEEE DOI
2112
Predictive models, Trajectory, Hidden Markov models,
Autonomous vehicles, Psychology, Legged locomotion,
datasets
BibRef
Ibrahim, M.R.[Mohamed R.],
Haworth, J.[James],
Christie, N.[Nicola],
Cheng, T.[Tao],
CyclingNet: Detecting cycling near misses from video streams in
complex urban scenes with deep learning,
IET-ITS(15), No. 10, 2021, pp. 1331-1344.
DOI Link
2109
action recognition, cycling near misses, deep learning, video streams
BibRef
Tran, T.T.M.[Tram Thi Minh],
Parker, C.[Callum],
Tomitsch, M.[Martin],
A Review of Virtual Reality Studies on Autonomous Vehicle-Pedestrian
Interaction,
HMS(51), No. 6, December 2021, pp. 641-652.
IEEE DOI
2112
Safety, Autonomous vehicles, Virtual reality, Autonomous vehicles,
external human-machine interfaces, pedestrians, virtual reality
BibRef
Galinskaite, L.[Lina],
Ulevicius, A.[Alius],
Valskys, V.[Vaidotas],
Samas, A.[Arunas],
Busher, P.E.[Peter E.],
Ignatavicius, G.[Gytautas],
The Influence of Landscape Structure on Wildlife-Vehicle
Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity
Relations,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link
2201
Animals, not people.
BibRef
Zhao, S.Z.[Sheng-Zhe],
Li, H.P.[Hao-Peng],
Ke, Q.H.[Qiu-Hong],
Liu, L.C.[Liang-Chen],
Zhang, R.[Rui],
Action-ViT: Pedestrian Intent Prediction in Traffic Scenes,
SPLetters(29), 2022, pp. 324-328.
IEEE DOI
2202
Transformers, Predictive models, Feature extraction, Roads,
Task analysis, Trajectory, Legged locomotion, Intention prediction,
temporal model
BibRef
Zhang, S.L.[Shi-Le],
Abdel-Aty, M.[Mohamed],
Wu, Y.[Yina],
Zheng, O.[Ou],
Pedestrian Crossing Intention Prediction at Red-Light Using Pose
Estimation,
ITS(23), No. 3, March 2022, pp. 2331-2339.
IEEE DOI
2203
Pose estimation, Videos, Trajectory, Legged locomotion, Vehicles,
Support vector machines, Safety, Pedestrian crossing intention,
artificial intelligence (AI)
BibRef
Iwata, T.[Tomoharu],
Shimizu, H.[Hitoshi],
Marumo, N.[Naoki],
Probabilistic Pedestrian Models for Estimating Unobserved Road
Populations,
ITS(23), No. 4, April 2022, pp. 3037-3047.
IEEE DOI
2204
Roads, Sociology, Statistics, Probabilistic logic, Tomography,
Gaussian distribution, Task analysis, Pedestrian modeling,
traffic simulator
BibRef
Feng, J.[Jian],
Wang, C.Y.[Chun-Yan],
Xu, C.[Can],
Kuang, D.[Dengming],
Zhao, W.[Wanzhong],
Active Collision Avoidance Strategy Considering Motion Uncertainty of
the pedestrian,
ITS(23), No. 4, April 2022, pp. 3543-3555.
IEEE DOI
2204
Collision avoidance, Planning, Safety, Trajectory, Uncertainty,
Acceleration, Injuries, Active collision avoidance,
multi-objective evaluation
BibRef
Domeyer, J.E.[Joshua E.],
Lee, J.D.[John D.],
Toyoda, H.[Heishiro],
Mehler, B.[Bruce],
Reimer, B.[Bryan],
Interdependence in Vehicle-Pedestrian Encounters and its Implications
for Vehicle Automation,
ITS(23), No. 5, May 2022, pp. 4122-4134.
IEEE DOI
2205
Roads, Automation, Safety, Vehicles, Measurement, Accidents, Instruments,
Human factors, automation, autonomous vehicles,
pedestrian
BibRef
Zhou, S.Y.[Si-Yuan],
Sun, X.[Xu],
Liu, B.J.[Bing-Jian],
Burnett, G.[Gary],
Factors Affecting Pedestrians' Trust in Automated Vehicles:
Literature Review and Theoretical Model,
HMS(52), No. 3, June 2022, pp. 490-500.
IEEE DOI
2205
Artificial intelligence, Roads, Automation, Uncertainty, Libraries,
Environmental factors, Bibliographies, trust
BibRef
Yang, B.[Biao],
Zhan, W.Q.[Wei-Qin],
Wang, P.[Pin],
Chan, C.Y.[Ching-Yao],
Cai, Y.F.[Ying-Feng],
Wang, N.[Nan],
Crossing or Not? Context-Based Recognition of Pedestrian Crossing
Intention in the Urban Environment,
ITS(23), No. 6, June 2022, pp. 5338-5349.
IEEE DOI
2206
Trajectory, Feature extraction, Skeleton, Autonomous automobiles,
Automobiles, Spatiotemporal phenomena, Crossing intention, self-driving car
BibRef
Held, P.[Patrick],
Steinhauser, D.[Dagmar],
Koch, A.[Andreas],
Brandmeier, T.[Thomas],
Schwarz, U.T.[Ulrich T.],
A Novel Approach for Model-Based Pedestrian Tracking Using Automotive
Radar,
ITS(23), No. 7, July 2022, pp. 7082-7095.
IEEE DOI
2207
Legged locomotion, Radar, Radar tracking, Kinematics,
Feature extraction, Radar cross-sections, Foot, data association
BibRef
Yin, N.[Nan],
Singh, A.K.[Amit Kumar],
Lv, H.B.[Hai-Bin],
Personalized Situation Adaptive Human-Vehicles-Interaction (HVI)
Prediction in COVID-19 Context,
ITS(23), No. 7, July 2022, pp. 9809-9818.
IEEE DOI
2207
Transportation, Context-aware services, Adaptation models,
COVID-19, Real-time systems, Mathematical models,
DC-LSTM
BibRef
Papini, G.P.R.[Gastone Pietro Rosati],
Plebe, A.[Alice],
da Lio, M.[Mauro],
Donŕ, R.[Riccardo],
A Reinforcement Learning Approach for Enacting Cautious Behaviours in
Autonomous Driving System: Safe Speed Choice in the Interaction With
Distracted Pedestrians,
ITS(23), No. 7, July 2022, pp. 8805-8822.
IEEE DOI
2207
Vehicles, Roads, Reinforcement learning, Trajectory, Neural networks,
Autonomous vehicles, Training, Vulnerable road users,
intelligent speed adaptation
BibRef
Shen, X.[Xun],
Raksincharoensak, P.[Pongsathorn],
Pedestrian-Aware Statistical Risk Assessment,
ITS(23), No. 7, July 2022, pp. 7910-7918.
IEEE DOI
2207
Modeling, Measurement, Predictive models, Risk management, Logistics,
Analytical models, Earthquakes, Near-accident event,
logistic regression
BibRef
Li, Y.H.[You-Huizi],
Yin, Y.[Yuyu],
Chen, X.[Xu],
Wan, J.[Jian],
Jia, G.Y.[Gang-Yong],
Sha, K.W.[Ke-Wei],
A Secure Dynamic Mix Zone Pseudonym Changing Scheme Based on Traffic
Context Prediction,
ITS(23), No. 7, July 2022, pp. 9492-9505.
IEEE DOI
2207
Safety, Trajectory, Roads, Vehicle dynamics, Real-time systems,
Accidents, Urban areas, Trajectory privacy, security,
traffic prediction
BibRef
Liu, Y.[Yishu],
Zhang, Q.[Qi],
Lv, Z.H.[Zhi-Han],
Real-Time Intelligent Automatic Transportation Safety Based on Big
Data Management,
ITS(23), No. 7, July 2022, pp. 9702-9711.
IEEE DOI
2207
Transportation, Big Data, Safety, Real-time systems,
Prediction algorithms, Sparks, Roads, Big data analytics,
DBN
BibRef
Manikandan, N.S.,
Kaliyaperumal, G.[Ganesan],
Collision avoidance approaches for autonomous mobile robots to tackle
the problem of pedestrians roaming on campus road,
PRL(160), 2022, pp. 112-121.
Elsevier DOI
2208
BibRef
Malik, F.A.[Faheem Ahmed],
Dala, L.[Laurent],
Busawon, K.[Krishna],
Intelligent Nanoscopic Cyclist Crash Modelling for Variable
Environmental Conditions,
ITS(23), No. 8, August 2022, pp. 11178-11189.
IEEE DOI
2208
Accidents, Safety, Computer crashes, Road safety, Predictive models,
Lighting, Mathematical model, Intelligent transportation system,
environmental conditions
BibRef
Hara, K.[Kensho],
Kataoka, H.[Hirokatsu],
Inaba, M.[Masaki],
Narioka, K.[Kenichi],
Hotta, R.[Ryusuke],
Satoh, Y.[Yutaka],
Predicting Appearance of Vehicles From Blind Spots Based on
Pedestrian Behaviors at Crossroads,
ITS(23), No. 8, August 2022, pp. 11917-11929.
IEEE DOI
2208
Videos, Spatiotemporal phenomena, Semantics, Cameras, Accidents,
Vehicles, Deep learning, future prediction, action recognition,
spatiotemporal 3D convolution
BibRef
Yang, B.[Bo],
Ning, J.[Jieqing],
Kaizuka, T.[Tsutomu],
Nishihira, M.[Munetaka],
Nakano, K.[Kimihiko],
Effects of Exterior Lighting System of Parked Vehicles on the
Behaviors of Cyclists,
ITS(23), No. 8, August 2022, pp. 12451-12463.
IEEE DOI
2208
Roads, Lighting, Accidents, Animation, Vehicles, Licenses, Safety, Cyclist,
exterior lighting system, parked vehicle
BibRef
Herman, M.[Michael],
Wagner, J.[Jörg],
Prabhakaran, V.[Vishnu],
Möser, N.[Nicolas],
Ziesche, H.[Hanna],
Ahmed, W.[Waleed],
Bürkle, L.[Lutz],
Kloppenburg, E.[Ernst],
Gläser, C.[Claudius],
Pedestrian Behavior Prediction for Automated Driving:
Requirements, Metrics, and Relevant Features,
ITS(23), No. 9, September 2022, pp. 14922-14937.
IEEE DOI
2209
Measurement, Trajectory, Predictive models, Task analysis,
Probabilistic logic, Vehicles, Mathematical models, machine learning
BibRef
Liu, Y.C.[Yen-Chen],
Jafari, A.[Alireza],
Shim, J.K.[Jae Kun],
Paley, D.A.[Derek A.],
Dynamic Modeling and Simulation of Electric Scooter Interactions With
a Pedestrian Crowd Using a Social Force Model,
ITS(23), No. 9, September 2022, pp. 16448-16461.
IEEE DOI
2209
Force, Motorcycles, Numerical models, Vehicle dynamics, Dynamics,
Automobiles, Mathematical models, Electric scooter,
Monte Carlo simulations
BibRef
Zang, G.Q.[Guo-Qin],
Azouigui, S.[Shéhérazade],
Saudrais, S.[Sébastien],
Hébert, M.[Mathieu],
Gonçalves, W.[Whilk],
Evaluating the Understandability of Light Patterns and Pictograms for
Autonomous Vehicle-to-Pedestrian Communication Functions,
ITS(23), No. 10, October 2022, pp. 18668-18680.
IEEE DOI
2210
Roads, Color, Symbols, Autonomous vehicles, Monitoring, Automation,
Autonomous vehicle, signal design
BibRef
Zhang, X.C.[Xing-Chen],
Angeloudis, P.[Panagiotis],
Demiris, Y.F.[Yi-Fannis],
ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for
Skeleton-Based Pedestrian Crossing Intention Prediction,
ITS(23), No. 11, November 2022, pp. 20773-20782.
IEEE DOI
2212
Skeleton, Feature extraction, Safety, Convolution,
Performance evaluation, Data mining, Benchmark testing,
intelligent vehicle
BibRef
Zhou, W.X.[Wei-Xuan],
Wang, X.S.[Xue-Song],
Calibrating and Comparing Autonomous Braking Systems in
Motorized-to-Non-Motorized-Vehicle Conflict Scenarios,
ITS(23), No. 11, November 2022, pp. 20636-20651.
IEEE DOI
2212
Accidents, Safety, Brakes, Vehicles, Bicycles, Automobiles, Data mining,
Automatic preventive braking, autonomous emergency braking,
safety-critical event
BibRef
Wu, W.S.[Wan-Shu],
Guo, J.H.[Jin-Han],
Ma, Z.Y.[Zi-Ying],
Zhao, K.[Kai],
Data-Driven Approach to Assess Street Safety: Large-Scale Analysis of
the Microscopic Design,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Ye, Q.M.[Qi-Ming],
Feng, Y.X.[Yu-Xiang],
Macias, J.J.E.[Jose Javier Escribano],
Stettler, M.[Marc],
Angeloudis, P.[Panagiotis],
Adaptive Road Configurations for Improved Autonomous
Vehicle-Pedestrian Interactions Using Reinforcement Learning,
ITS(24), No. 2, February 2023, pp. 2024-2034.
IEEE DOI
2302
Roads, Aerospace electronics, Optimization, Costs, Space exploration,
Microscopy, Heuristic algorithms, Autonomous vehicles, pedestrians,
infrastructure management
BibRef
Chen, K.[Kai],
Zhu, H.H.[Hai-Hua],
Tang, D.[Dunbing],
Zheng, K.[Kun],
Future pedestrian location prediction in first-person videos for
autonomous vehicles and social robots,
IVC(134), 2023, pp. 104671.
Elsevier DOI
2305
Social intention, Human-vehicle interactions,
First-person videos, Image depth, Social spatial dependencies, Transformer
BibRef
Liu, W.[Wen],
Shao, Y.X.[Yi-Xiao],
Zhai, S.H.[Shi-Hong],
Yang, Z.[Zhao],
Chen, P.S.[Pei-Shuai],
Computer Vision-Based Tracking of Workers in Construction Sites Based
on MDNet,
IEICE(E106-D), No. 5, May 2023, pp. 653-661.
WWW Link.
2305
BibRef
Huang, R.[Rong],
Zhao, X.[Xuan],
Yuan, Y.F.[Yu-Fei],
Yu, Q.[Qiang],
Liu, C.Q.[Cheng-Qing],
Daamen, W.[Winnie],
Modeling Pedestrian Tactical and Operational Decisions Under Risk and
Uncertainty: A Two-Layer Model Framework,
ITS(24), No. 5, May 2023, pp. 5259-5281.
IEEE DOI
2305
Decision making, Uncertainty, Computational modeling,
Mathematical models, Numerical models, Sensitivity analysis,
cellular automaton
BibRef
Zhang, X.C.[Xing-Chen],
Angeloudis, P.[Panagiotis],
Demiris, Y.F.[Yi-Fannis],
Dual-branch spatio-temporal graph neural networks for pedestrian
trajectory prediction,
PR(142), 2023, pp. 109633.
Elsevier DOI
2307
Pedestrian trajectory prediction, Social interactions,
Graph convolutional networks, Graph attention networks, Spatio-temporal graph
BibRef
Zhai, X.L.[Xiao-Lin],
Hu, Z.X.[Zheng-Xi],
Yang, D.Y.[Ding-Ye],
Zhou, L.[Lei],
Liu, J.[Jingtai],
Social Aware Multi-modal Pedestrian Crossing Behavior Prediction,
ACCV22(IV:275-290).
Springer DOI
2307
BibRef
Wang, Y.N.[Yu-Ning],
Huang, H.[Heye],
Zhang, B.[Bo],
Wang, J.Q.[Jian-Qiang],
A differentiated decision-making algorithm for automated vehicles
based on pedestrian feature estimation,
IET-ITS(17), No. 7, 2023, pp. 1454-1466.
DOI Link
2307
automated vehicles, feature estimation, decision-making,
pedestrian, interaction
BibRef
Lin, M.C.[Ming-Chih],
Lin, Y.C.[Yu-Chen],
Hung, M.K.[Ming-Ku],
Pedestrian potentially dangerous behaviour prediction based on
attention-long-short-term memory with egocentric vision,
IET-ITS(17), No. 7, 2023, pp. 1331-1343.
DOI Link
2307
advanced driver assistance systems, artificial intelligence,
image recognition, perception
BibRef
Zhang, C.[Chi],
Berger, C.[Christian],
Pedestrian Behavior Prediction Using Deep Learning Methods for Urban
Scenarios: A Review,
ITS(24), No. 10, October 2023, pp. 10279-10301.
IEEE DOI
2310
BibRef
Melotti, G.[Gledson],
Lu, W.H.[Wei-Hao],
Conde, P.[Pedro],
Zhao, D.[Dezong],
Asvadi, A.[Alireza],
Gonçalves, N.[Nuno],
Premebida, C.[Cristiano],
Probabilistic Approach for Road-Users Detection,
ITS(24), No. 9, September 2023, pp. 9253-9267.
IEEE DOI
2310
BibRef
Manikandan, N.S.,
Ganesan, K.,
Energy-aware vehicle/pedestrian detection and close movement alert at
nighttime in dense slow traffic on Indian urban roads using a depth
camera,
IJCVR(13), No. 6, 2023, pp. 658-676.
DOI Link
2310
BibRef
Li, P.[Pei],
Guo, H.Z.[Hui-Zhong],
Bao, S.[Shan],
Kusari, A.[Arpan],
A Probabilistic Framework for Estimating the Risk of
Pedestrian-Vehicle Conflicts at Intersections,
ITS(24), No. 12, December 2023, pp. 14111-14120.
IEEE DOI
2312
BibRef
Veluchamy, S.,
Mahesh, K.M.[K. Michael],
Muthukrishnan, R.,
Karthi, S.,
HY-LSTM: A new time series deep learning architecture for estimation
of pedestrian time to cross in advanced driver assistance system,
JVCIR(97), 2023, pp. 103982.
Elsevier DOI
2312
Advanced driver assistance system, Pedestrian time, LSTM, OCNN,
Deep joint segmentation
BibRef
Zhou, W.[Wei],
Liu, Y.Q.[Yu-Qing],
Zhao, L.[Lei],
Xu, S.[Sixuan],
Wang, C.[Chen],
Pedestrian Crossing Intention Prediction From Surveillance Videos for
Over-the-Horizon Safety Warning,
ITS(25), No. 2, February 2024, pp. 1394-1407.
IEEE DOI
2402
Pedestrians, Surveillance, Trajectory, Cameras, Feature extraction,
Safety, Predictive models, Traffic safety,
environment graph
BibRef
Wang, M.X.[Ming-Xi],
Li, L.L.[Lei-Lei],
Liu, J.B.[Jing-Bin],
Chen, R.Z.[Rui-Zhi],
Neural Network Aided Factor Graph Optimization for Collaborative
Pedestrian Navigation,
ITS(25), No. 1, January 2024, pp. 303-314.
IEEE DOI
2402
Pedestrians, Navigation, Collaboration, Sensors,
Global navigation satellite system, Artificial neural networks,
factor graph optimization
BibRef
Tian, K.[Kai],
Markkula, G.[Gustav],
Wei, C.[Chongfeng],
Lee, Y.M.[Yee Mun],
Madigan, R.[Ruth],
Hirose, T.[Toshiya],
Merat, N.[Natasha],
Romano, R.[Richard],
Deconstructing Pedestrian Crossing Decisions in Interactions With
Continuous Traffic: An Anthropomorphic Model,
ITS(25), No. 3, March 2024, pp. 2466-2478.
IEEE DOI
2405
Pedestrians, Roads, Computational modeling, Visualization,
Decision making, Visual perception, Psychology, traffic flow
BibRef
Zhang, Z.Y.[Zhen-Yuan],
Lai, H.Z.[Hui-Zhen],
Huang, D.[Darong],
Fang, X.[Xin],
Zhou, M.[Mu],
Zhang, Y.[Ying],
RETA: 4D Radar-Based End-to-End Joint Tracking and Activity
Estimation for Low-Observable Pedestrian Safety in Cluttered Traffic
Scenarios,
ITS(25), No. 5, May 2024, pp. 4413-4426.
IEEE DOI
2405
Radar tracking, Pedestrians, Radar, Radar cross-sections,
Feature extraction, Target tracking, Activity recognition, FMCW radar
BibRef
Wu, J.Y.[Jing-Yuan],
Ruenz, J.[Johannes],
Berkemeyer, H.[Hendrik],
Dixon, L.[Liza],
Althoff, M.[Matthias],
Goal-Oriented Pedestrian Motion Prediction,
ITS(25), No. 6, June 2024, pp. 5282-5298.
IEEE DOI
2406
Pedestrians, Trajectory, Vehicle dynamics, Planning,
Behavioral sciences, Markov processes, Mathematical models,
pedestrian motion prediction
BibRef
Li, S.W.[Shi-Wei],
Li, Q.Q.[Qian-Qian],
Xu, J.[Jiao],
Zhang, Y.Z.[Yu-Zhao],
Simulation of cross-pedestrian flow in intersection based on
direction fuzzy visual field,
IET-ITS(18), No. 6, 2024, pp. 1045-1067.
DOI Link
2406
cellular automata, fuzzy systems, pedestrians, simulation
BibRef
Kitchat, K.[Kotcharat],
Chiu, Y.L.[Yi-Lun],
Lin, Y.C.[Yu-Chiu],
Sun, M.T.[Min-Te],
Wada, T.[Tomotaka],
Sakai, K.[Kazuya],
Ku, W.S.[Wei-Shinn],
Wu, S.C.[Shiaw-Chian],
Jeng, A.A.K.[Andy An-Kai],
Liu, C.H.[Ching-Hao],
PedCross: Pedestrian Crossing Prediction for Auto-Driving Bus,
ITS(25), No. 8, August 2024, pp. 8730-8740.
IEEE DOI
2408
Pedestrians, Skeleton, Predictive models, Long short term memory,
Hidden Markov models, YOLO, Trajectory, pose detection
BibRef
Chen, X.B.[Xiao-Bo],
Zhang, S.L.[Shi-Lin],
Li, J.[Jun],
Yang, J.[Jian],
Pedestrian Crossing Intention Prediction Based on Cross-Modal
Transformer and Uncertainty-Aware Multi-Task Learning for Autonomous
Driving,
ITS(25), No. 9, September 2024, pp. 12538-12549.
IEEE DOI Code:
WWW Link.
2409
Pedestrians, Predictive models, Transformers, Task analysis,
Multitasking, Feature extraction, Data models,
homoscedastic uncertainty
BibRef
Luo, Y.[Yan],
Zhao, M.[Muming],
Sun, J.[Jun],
Zhai, G.T.[Guang-Tao],
Zhang, C.Y.[Chong-Yang],
Consistent GT-Proposal Assignment for Challenging Pedestrian
Detection,
MultMed(26), 2024, pp. 9398-9409.
IEEE DOI
2410
Proposals, Pedestrians, Location awareness, Detectors, Training,
Measurement, Pedestrian detection, label assignment, depth distribution
BibRef
Papathanasopoulou, V.[Vasileia],
Perakis, H.[Harris],
Spyropoulou, I.[Ioanna],
Gikas, V.[Vassilis],
Pedestrian Simulation Challenges: Modeling Techniques and Emerging
Positioning Technologies for ITS Applications,
ITS(25), No. 10, October 2024, pp. 12876-12892.
IEEE DOI
2410
Pedestrians, Data models, Computational modeling, Predictive models,
Force, Mathematical models, Pedestrian behavior, simulation performance
BibRef
Park, S.[Sungjune],
Kim, H.[Hyunjun],
Ro, Y.M.[Yong Man],
Integrating Language-Derived Appearance Elements With Visual Cues in
Pedestrian Detection,
CirSysVideo(34), No. 9, September 2024, pp. 7975-7985.
IEEE DOI
2410
Pedestrians, Visualization, Task analysis, Detectors, Surveillance,
Safety, Image classification, Pedestrian detection,
language-derived appearance element
BibRef
Han, Y.J.[Young-Jun],
Lee, H.[Hasik],
Impact of Smartphone Activity on Pedestrian Safety:
A Case Study in Seoul,
ITS(25), No. 10, October 2024, pp. 13194-13203.
IEEE DOI
2410
Pedestrians, Accidents, Safety, Legged locomotion, Transportation,
Surveys, Solid modeling, Pedestrian accident, smartphone data,
multiple regression
BibRef
Li, H.J.[Hao-Jie],
Jin, Y.[Yuan],
Ren, G.[Gang],
Interpretable Prediction of Pedestrian Crossing Intention: Fusion of
Human Skeletal Information in Natural Driving Scenarios,
ITS(25), No. 11, November 2024, pp. 18153-18170.
IEEE DOI
2411
Pedestrians, Feature extraction, Accuracy, Predictive models,
Skeleton, Vehicle dynamics, Prediction algorithms, machine learning
BibRef
Dai, S.Y.[Si-Yang],
Liu, J.[Jun],
Cheung, N.M.[Ngai-Man],
Uncertainty-Aware Pedestrian Crossing Prediction via Reinforcement
Learning,
CirSysVideo(34), No. 10, October 2024, pp. 9540-9549.
IEEE DOI
2411
Uncertainty, Pedestrians, Predictive models,
Reinforcement learning, Labeling, Roads, Trajectory,
uncertainty estimation
BibRef
Perrusquía, A.[Adolfo],
Wei, Z.[Zhuangkun],
Guo, W.S.[Wei-Si],
Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode
Decomposition,
SMCS(54), No. 12, December 2024, pp. 7897-7908.
IEEE DOI
2411
Trajectory, Autonomous systems, Predictive models,
Prediction algorithms, Heuristic algorithms, Data models,
trajectory intent prediction
BibRef
Falaschetti, L.[Laura],
Manoni, L.[Lorenzo],
Palma, L.[Lorenzo],
Pierleoni, P.[Paola],
Turchetti, C.[Claudio],
Embedded Real-Time Vehicle and Pedestrian Detection Using a
Compressed Tiny YOLO v3 Architecture,
ITS(25), No. 12, December 2024, pp. 19399-19414.
IEEE DOI
2412
YOLO, Pedestrians, Real-time systems, Accuracy, Proposals,
Detectors, Autonomous driving, CNN compression, pedestrian detection
BibRef
Osman, N.[Nada],
Camporese, G.[Guglielmo],
Ballan, L.[Lamberto],
Multi-modal transformer with language modality distillation for early
pedestrian action anticipation,
CVIU(249), 2024, pp. 104144.
Elsevier DOI
2412
Action anticipation, Pedestrian intent prediction,
Language-vision models, Image-to-video captioning, Multi-modal transformers
BibRef
Yang, B.[Biao],
Wei, Z.W.[Zhi-Wen],
Hu, C.[Chuan],
Cai, Y.F.[Ying-Feng],
Wang, H.[Hai],
Hu, H.Y.[Hong-Yu],
Real-Time Pedestrian Crossing Anticipation Based on an
Action-Interaction Dual-Branch Network,
ITS(25), No. 12, December 2024, pp. 21021-21034.
IEEE DOI Code:
WWW Link.
2412
Pedestrians, Real-time systems, Feature extraction,
Spatiotemporal phenomena, Skeleton, Semantics, Topology, Convolution,
traffic-object interaction
BibRef
Tan, S.H.[Shu-Hang],
Wang, Z.L.[Zhi-Ling],
Zhong, Y.[Yan],
RCP-RF: A comprehensive road-car-pedestrian risk management framework
based on driving risk potential field,
IET-ITS(18), No. 12, 2024, pp. 2618-2640.
DOI Link
2501
automated driving and intelligent vehicles, risk analysis
BibRef
Manzour, M.A.[Mohamed A.],
Elias, C.M.[Catherine M.],
Morgan, E.I.[Elsayed I.],
Shehata, O.M.[Omar M.],
Development of a Modular ROS-Enabled Pedestrian Intention Prediction
Architecture for AVs Maneuvering Control,
ITS(26), No. 1, January 2025, pp. 798-809.
IEEE DOI
2501
Pedestrians, Predictive models, Roads, Visual perception, Trajectory,
Hardware, Planning, Visualization, Vehicles, Mechatronics,
vehicle control
BibRef
Colombo, A.[Alessandro],
Depaola, M.[Matteo],
Ferrise, F.[Francesco],
Dozio, N.[Nicolň],
de Campos, G.R.[Gabriel Rodrigues],
Predicting Mispredictions: A Model of Human Misjudgment About
Vulnerable Road Users' Trajectories,
ITS(26), No. 1, January 2025, pp. 157-168.
IEEE DOI
2501
Vehicles, Solid modeling, Bicycles, Mathematical models,
Computational modeling, Cognitive science, Predictive models,
vulnerable road users
BibRef
Feng, Y.[Yan],
Xu, Z.[Zhenlin],
Farah, H.[Haneen],
van Arem, B.[Bart],
Does Another Pedestrian Matter? A Virtual Reality Study on the
Interaction Between Multiple Pedestrians and Autonomous Vehicles in
Shared Space,
ITS(26), No. 1, January 2025, pp. 196-209.
IEEE DOI
2501
Pedestrians, Roads, Space vehicles, Lighting, Autonomous vehicles,
Virtual environments, Vehicle dynamics, Ethics, Uncertainty, Turning,
virtual reality
BibRef
Wang, M.[MeiJun],
Meng, Y.[Yu],
Xu, Y.[Yan],
Li, Q.[Quan],
Nie, B.B.[Bing-Bing],
Real-Time Reconstruction of Multi-Body Pedestrian Pre-Impact Posture
in Collision Accidents From Monocular Images,
ITS(26), No. 1, January 2025, pp. 457-471.
IEEE DOI
2501
Pedestrians, Accidents, Injuries, Image reconstruction, Videos,
Pose estimation, Computational modeling, Shape, Real-time systems,
reconstruction of pedestrian-vehicle collision
BibRef
Costa, M.[Miguel],
Marques, M.[Manuel],
Azevedo, C.L.[Carlos Lima],
Siebert, F.W.[Felix Wilhelm],
Moura, F.[Filipe],
Which Cycling Environment Appears Safer? Learning Cycling Safety
Perceptions From Pairwise Image Comparisons,
ITS(26), No. 2, February 2025, pp. 1689-1700.
IEEE DOI
2502
Safety, Roads, Urban areas, Surveys, Feature extraction, Data models,
Solid modeling, Technology management, Technological innovation
BibRef
Qin, J.R.[Jia-Rui],
Feng, Y.Z.[Yi-Zhe],
Sheng, Y.[Yehua],
Huang, Y.[Yi],
Zhang, F.Y.[Feng-Yuan],
Zhang, K.X.[Kai-Xuan],
Evaluation of Pedestrian-Perceived Comfort on Urban Streets Using
Multi-Source Data: A Case Study in Nanjing, China,
IJGI(14), No. 2, 2025, pp. 63.
DOI Link
2502
BibRef
Abbasi, J.A.[Jibran A.],
Parsi, A.[Ashkan],
Ringelstein, N.[Nicolas],
Reilhac, P.[Patrice],
Jones, E.[Edward],
Glavin, M.[Martin],
Enhancing Cyclist Safety Through Driver Gaze Analysis at
Intersections With Cycle Lanes,
ITS(26), No. 3, March 2025, pp. 3175-3184.
IEEE DOI
2503
Vehicles, Mirrors, Roads, Turning, Bicycles, Urban areas, Junctions, Injuries,
Sensors, Driver inattention, mirror checking detection, nearside-turns
BibRef
Abdi, B.[Behzad],
Mirzaei, S.[Sara],
Adl, M.[Morteza],
Hidajat, S.[Severin],
Emadi, A.[Ali],
Advancing Vulnerable Road Users Safety: Interdisciplinary Review on
V2X Communication and Trajectory Prediction,
ITS(26), No. 3, March 2025, pp. 2921-2943.
IEEE DOI
2503
Safety, Vehicle-to-everything, Trajectory, Accidents, Reviews,
Pedestrians, Predictive models, Sensors, Focusing, Vehicles,
vulnerable road user (VRU)
BibRef
Tian, C.[Cheng],
Huang, C.[Chao],
Wang, Y.[Yan],
Chung, E.[Edward],
Nguyen, A.T.[Anh-Tu],
Wong, P.K.[Pak Kin],
Ni, W.[Wei],
Jamalipour, A.[Abbas],
Li, K.[Kai],
Huang, H.L.[Hai-Long],
Recent Estimation Techniques of Vehicle-Road-Pedestrian States for
Traffic Safety: Comprehensive Review and Future Perspectives,
ITS(26), No. 3, March 2025, pp. 2897-2920.
IEEE DOI
2503
Roads, Vehicle dynamics, Surveys, Pedestrians, Tires,
Intelligent transportation systems, Dynamics, State estimation,
intelligent transportation systems
BibRef
Mushtaq, H.[Husnain],
Deng, X.H.[Xiao-Heng],
Jiang, P.[Ping],
Wan, S.H.[Shao-Hua],
Ali, M.[Mubashir],
Ullah, I.[Irshad],
GFA-SMT: Geometric Feature Aggregation and Self-Attention in a
Multi-Head Transformer for 3D Object Detection in Autonomous Vehicles,
ITS(26), No. 3, March 2025, pp. 3557-3573.
IEEE DOI
2503
Feature extraction, Point cloud compression, Object detection,
Accuracy, Semantics, Autonomous vehicles, Transformers, Pedestrians,
autonomous driving
BibRef
Elgazwy, A.[Ahmed],
Elgazzar, K.[Khalid],
Khamis, A.[Alaa],
Predicting Pedestrian Crossing Intentions in Adverse Weather With
Self-Attention Models,
ITS(26), No. 3, March 2025, pp. 3250-3261.
IEEE DOI
2503
Pedestrians, Feature extraction, Visualization, Predictive models,
Transformers, Meteorology, Accuracy, Image enhancement, Videos,
assisted and automated driving vehicles
BibRef
Cui, G.[Gege],
Lu, C.[Chao],
Liu, Y.[Yupei],
Wang, J.B.[Jun-Bin],
Meng, X.[Xianghao],
Gong, J.W.[Jian-Wei],
Risk Assessment of Cyclists in the Mixed Traffic Based on Multilevel
Graph Representation,
ITS(26), No. 4, April 2025, pp. 5196-5210.
IEEE DOI
2504
Risk management, Trajectory, Feature extraction, Safety,
Predictive models, Behavioral sciences, Analytical models,
autonomous vehicles
BibRef
Jekl, B.[Bence],
Dabcevic, Z.[Zvonimir],
Németh, B.[Balázs],
kugor, B.[Branimir],
Gáspár, P.[Péter],
Scenario-Optimization-Based Velocity Planning of Autonomous Vehicles
for Interacting With Pedestrians,
ITS(26), No. 4, April 2025, pp. 5382-5395.
IEEE DOI
2504
Pedestrians, Planning, Computational modeling, Safety, Data models,
Real-time systems, Predictive models, Autonomous vehicles, data-driven control
BibRef
Antariksa, G.[Gian],
Tamakloe, R.[Reuben],
Liu, J.[Jinli],
Das, S.[Subasish],
Automated and Explainable Artificial Intelligence to Enhance
Prediction of Pedestrian Injury Severity,
ITS(26), No. 4, April 2025, pp. 5568-5584.
IEEE DOI
2504
Predictive models, Pedestrians, Injuries, Explainable AI,
Biological system modeling, Accidents, Analytical models, Accuracy,
explainable AI
BibRef
Liang, S.D.[Shi-Dong],
Zhao, T.Y.[Tian-Yu],
Zhao, J.[Jing],
Pedestrian-Vehicle Interaction Analysis Based on Concept of Dynamic
Straight-Right Lane at Signalized Intersection,
ITS(26), No. 5, May 2025, pp. 6017-6041.
IEEE DOI
2505
Pedestrians, Roads, Safety, Delays, Autonomous vehicles, Green products,
Vehicle dynamics, Trajectory, Urban areas, traffic efficiency
BibRef
Xie, C.[Chen],
Lin, C.[Ciyun],
Zheng, X.Y.[Xiao-Yu],
Gong, B.[Bowen],
López, A.M.[Antonio M.],
GTransPDM: A Graph-Embedded Transformer With Positional Decoupling
for Pedestrian Crossing Intention Prediction,
SPLetters(32), 2025, pp. 2109-2113.
IEEE DOI
2505
Pedestrians, Transformers, Skeleton, Feature extraction, Training,
Dynamics, Vehicle dynamics, Safety, Roads, traffic safety
BibRef
Tian, K.[Kai],
Wei, C.[Chongfeng],
Lyu, W.[Wei],
Wang, Y.Y.[Yue-Yang],
Lee, Y.M.[Yee Mun],
Merat, N.[Natasha],
Romano, R.[Richard],
Markkula, G.[Gustav],
Interacting with Yielding Vehicles: A Perceptually Plausible Model
for Pedestrian Road Crossing Decisions,
ITS(26), No. 7, July 2025, pp. 9654-9668.
IEEE DOI
2507
Pedestrians, Roads, Computational modeling, Games, Legged locomotion,
Kinematics, Solid modeling, Safety, Decision making, simulation
BibRef
Azarmi, M.[Mohsen],
Rezaei, M.[Mahdi],
Wang, H.[He],
PIP-Net: Pedestrian Intention Prediction in the Wild,
ITS(26), No. 7, July 2025, pp. 9824-9837.
IEEE DOI
2507
Pedestrians, Roads, Visualization, Feature extraction,
Predictive models, Cameras, Vehicle dynamics, Data models,
deep neural networks
BibRef
Da, F.[Fang],
Probabilistic Planning With Comprehensive Reactive Safety,
ITS(26), No. 7, July 2025, pp. 9241-9250.
IEEE DOI
2507
Trajectory, Uncertainty, Planning, Safety, Probabilistic logic,
Autonomous vehicles, Pedestrians, Roads, Turning, Training,
probabilistic prediction
BibRef
Dang, M.[Meiting],
Zhao, D.[Dezong],
Wang, Y.F.[Ya-Fei],
Wei, C.[Chongfeng],
Dynamic Game-Theoretical Decision-Making Framework for
Vehicle-Pedestrian Interaction With Human Bounded Rationality,
ITS(26), No. 7, July 2025, pp. 10822-10833.
IEEE DOI
2507
Pedestrians, Cognition, Vehicle dynamics, Uncertainty,
Computational modeling, Roads, Nash equilibrium, Navigation, Games,
bounded rationality
BibRef
González, C.[Cristina],
Ayobi, N.[Nicolás],
Escallón, F.[Felipe],
Baldovino-Chiquillo, L.[Laura],
Wilches-Mogollón, M.[Maria],
Pasos, D.[Donny],
Ramírez, N.[Nicole],
Pinzón, J.[Jose],
Sarmiento, O.[Olga],
Quistberg, D.A.[D. Alex],
Arbeláez, P.[Pablo],
STRIDE: Street View-based Environmental Feature Detection and
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WACV23(950-960)
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Visualization, Uncertainty, Navigation, Training data,
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Feature extraction, Trajectory, Behavioral sciences,
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ICIP22(3446-3450)
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2211
Measurement, Visualization, Protocols, Estimation, Predictive models,
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ICCV21(15213-15222)
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2203
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ICCV21(15580-15590)
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2203
Semantics, Gesture recognition, Predictive models,
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AVVision21(3096-3102)
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Convolutional codes, Visualization, Roads, Pose estimation,
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SoMoF21(2304-2311)
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Visualization, Navigation, Convolution,
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Computational modeling, Urban areas,
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Deep learning, Measurement, Pipelines, Prototypes,
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Measurement, Meters, System performance,
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ICPR21(1788-1795)
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Legged locomotion, Space vehicles, Training, Solid modeling,
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MMHUA20(123-135).
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WACV20(765-774)
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A Rapid Recognition Method for Pedestrian Abnormal Behavior,
CVIDL20(241-245)
IEEE DOI
2102
convolutional neural nets, feature extraction,
image fusion, image recognition, image sequences,
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An optimisation Technique for the Detection of Safety Attributes
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IVCNZ20(1-6)
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Industries, Evolutionary computation, Road safety, Safety,
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HBU19(1179-1187)
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behavioural sciences computing, entropy,
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Trabelsi, A.,
Blanchard, N.,
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WACV20(2286-2295)
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Predictive models, Hidden Markov models, Feature extraction,
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Latent Linear Dynamics for Modeling Pedestrian Behaviors,
ICPR18(1592-1597)
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1812
Trajectory, Data models, Clustering algorithms,
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IVIC17(279-289).
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1711
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MVA17(416-419)
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1708
Autonomous vehicles, Benchmark testing,
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Traffic17(898-905)
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1709
Cameras, Feature extraction, Safety, Sensors, Surveillance, Tracking, Videos
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ICCV17(4960-4969)
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1802
feature extraction, image segmentation,
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CVPR14(2553-2560)
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1409
geodesic active contours; pedestrian behavior; trajectory analysis
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Adaptive Parameter Estimation of Person Recognition Model in a
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Earlier:
Multiple Human Tracking In Complex Situation By Data Assimilation With
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ISPRS12(XXXIX-B3:409-414).
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AVSBS11(539-542).
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Detection, Tracking, Infrared, IR, Thermal Images .