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HTML Version. News item refrencing SLR Engineering work.
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ITS(14), No. 2, 2013, pp. 720-730.
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1307
Navigation; Safety; Tracking; Autonomous vehicles; pedestrian detection
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1309
Advanced driver assistance system (ADAS)
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Scene-based pedestrian safety performance model in mixed traffic
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1407
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1404
BibRef
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Unsupervised domain adaptation of virtual and real worlds for
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1302
Accuracy
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Vision-based Pedestrian Protection Systems for Intelligent Vehicles,
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A cheaper way for robocars to avoid pedestrians,
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1507
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Nakagawa, C.,
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Evaluation of the Effects of a Personal Mobility Vehicle on Multiple
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ITS(16), No. 4, August 2015, pp. 2028-2037.
IEEE DOI
1508
Indexes
BibRef
Said, Y.,
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Efficient and high-performance pedestrian detector implementation for
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IET-ITS(10), No. 6, 2016, pp. 438-444.
DOI Link
1608
computer vision
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IET-IPR(11), No. 10, October 2017, pp. 833-840.
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1710
BibRef
Jeong, M.,
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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.
DOI Link
1805
BibRef
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Aoki, S.,
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Tools and Methodologies for Autonomous Driving Systems,
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IEEE DOI
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.,
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Carruth, D.W.,
Understanding Human Response to the Presence and Actions of Unmanned
Ground Vehicle Systems in Field Environment,
HMS(48), No. 4, August 2018, pp. 325-336.
IEEE DOI
1808
human-robot interaction, mobile robots, pedestrians,
remotely operated vehicles, sport, video signal processing,
remotely operated vehicles
BibRef
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)
BibRef
Flores, C.,
Merdrignac, P.,
de Charette, R.,
Navas, F.,
Milanés, V.,
Nashashibi, F.,
A Cooperative Car-Following/Emergency Braking System With
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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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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
BibRef
Deb, S.,
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How Communicating Features can Help Pedestrian Safety in the Presence
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HMS(50), No. 2, April 2020, pp. 176-186.
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, Computer architecture,
pedestrian flow
BibRef
Yang, L.[Lie],
Hu, G.H.[Guang-Hua],
Song, Y.H.[Yong-Hao],
Li, G.F.[Guo-Feng],
Xie, L.H.[Long-Han],
Intelligent video analysis: A Pedestrian trajectory extraction method
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CVIU(196), 2020, pp. 102968.
Elsevier DOI
2006
Fisheye camera, Pedestrian detection, Object tracking,
Height estimation, Trajectory extraction
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.
DOI Link
2008
BibRef
Zhou, Z.P.[Zhu-Ping],
Peng, Y.L.[Yun-Long],
Cai, Y.F.[Yi-Fei],
Vision-based approach for predicting the probability of
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IET-ITS(14), No. 11, November 2020, pp. 1447-1455.
DOI Link
2010
BibRef
Elhenawy, M.[Mohammed],
Ashqar, H.I.[Huthaifa I.],
Masoud, M.[Mahmoud],
Almannaa, M.H.[Mohammed H.],
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.
DOI Link
2011
BibRef
Hou, L.,
Xin, L.,
Li, S.E.,
Cheng, B.,
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
BibRef
Zhou, C.J.[Cheng-Ju],
Wu, M.Q.[Mei-Qing],
Lam, S.K.[Siew-Kei],
Group Cost-Sensitive BoostLR With Vector Form Decorrelated Filters
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ITS(21), No. 12, December 2020, pp. 5022-5035.
IEEE DOI
2012
Feature extraction, Decorrelation, Training,
Computational complexity, Testing, Boosting, Pedestrian detection,
BibRef
Haddad, S.[Sirin],
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Self-Growing Spatial Graph Network for Context-Aware Pedestrian
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ICIP21(1029-1033)
IEEE DOI
2201
BibRef
Earlier:
Self-Growing Spatial Graph Networks for Pedestrian Trajectory
Prediction,
WACV20(1140-1148)
IEEE DOI
2006
Adaptation models, Visualization, Adaptive systems,
Machine learning, Predictive models, Spatial databases, Trajectory,
Nonnegative Matrix Factorization.
Trajectory, Predictive models, Task analysis, Dynamics, Data models
BibRef
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Tsotsos, J.K.,
Autonomous Vehicles That Interact With Pedestrians:
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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
BibRef
Gilroy, S.[Shane],
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
BibRef
Pugh, N.[Nigel],
Park, H.[Hyoshin],
Derjany, P.[Pierrot],
Liu, D.[Dahai],
Namilae, S.[Sirish],
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
BibRef
Wang, R.P.[Rui-Ping],
Cui, Y.[Yong],
Song, X.[Xiao],
Chen, K.[Kai],
Fang, H.[Hong],
Multi-information-based convolutional neural network with attention
mechanism for pedestrian trajectory prediction,
IVC(107), 2021, pp. 104110.
Elsevier DOI
2103
Depth map, Pose, 2D-3D size information,
Convolutional neural network, Trajectory prediction
BibRef
Song, X.[Xiao],
Chen, K.[Kai],
Li, X.[Xu],
Sun, J.[Jinghan],
Hou, B.C.[Bao-Cun],
Cui, Y.[Yong],
Zhang, B.C.[Bao-Chang],
Xiong, G.[Gang],
Wang, Z.L.[Zi-Lie],
Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM
Network,
ITS(22), No. 6, June 2021, pp. 3285-3302.
IEEE DOI
2106
Trajectory, Predictive models, Neural networks, Force,
Mathematical model, Feature extraction, Tensors,
neural network
BibRef
Yu, K.P.[Ke-Ping],
Lin, L.[Long],
Alazab, M.[Mamoun],
Tan, L.[Liang],
Gu, B.[Bo],
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
BibRef
Xu, Q.[Qing],
Wu, H.[Haoran],
Wang, J.Q.[Jian-Qiang],
Xiong, H.[Hui],
Liu, J.X.[Jin-Xin],
Li, K.Q.[Ke-Qiang],
Roadside pedestrian motion prediction using Bayesian methods and
particle filter,
IET-ITS(15), No. 9, 2021, pp. 1167-1182.
DOI Link
2108
BibRef
Camara, F.[Fanta],
Bellotto, N.[Nicola],
Cosar, S.[Serhan],
Nathanael, D.[Dimitris],
Althoff, M.[Matthias],
Wu, J.[Jingyuan],
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.[Jingyuan],
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
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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
Chen, T.[Tina],
Tian, R.[Renran],
Ding, Z.[Zhengming],
Visual Reasoning using Graph Convolutional Networks for Predicting
Pedestrian Crossing Intention,
AVVision21(3096-3102)
IEEE DOI
2112
Convolutional codes, Visualization, Roads, Pose estimation,
Predictive models, Feature extraction, Cognition
BibRef
Singh, A.[Ankur],
Suddamalla, U.[Upendra],
Multi-Input Fusion for Practical Pedestrian Intention Prediction,
SoMoF21(2304-2311)
IEEE DOI
2112
Visualization, Navigation, Convolution,
Roads, Video sequences
BibRef
Bhattacharyya, A.[Apratim],
Reino, D.O.[Daniel Olmeda],
Fritz, M.[Mario],
Schiele, B.[Bernt],
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers,
CVPR21(6404-6413)
IEEE DOI
2111
Computational modeling, Urban areas,
Predictive models, Turning, Trajectory, Pattern recognition
BibRef
Feifel, P.[Patrick],
Bonarens, F.[Frank],
Köster, F.[Frank],
Reevaluating the Safety Impact of Inherent Interpretability on Deep
Neural Networks for Pedestrian Detection,
SAIAD21(29-37)
IEEE DOI
2109
Deep learning, Measurement, Pipelines, Prototypes,
Semisupervised learning, Cognition, Software
BibRef
Lyssenko, M.[Maria],
Gladisch, C.[Christoph],
Heinzemann, C.[Christian],
Woehrle, M.[Matthias],
Triebel, R.[Rudolph],
From Evaluation to Verification: Towards Task-oriented Relevance
Metrics for Pedestrian Detection in Safety-critical Domains,
SAIAD21(38-45)
IEEE DOI
2109
Measurement, Meters, System performance,
Pattern recognition, Proposals, Motion measurement
BibRef
Shimizu, T.[Takahiro],
Koide, K.[Kenji],
Oishi, S.J.[Shun-Ji],
Yokozuka, M.[Masashi],
Banno, A.[Atsuhiko],
Shino, M.[Motoki],
Sensor-independent Pedestrian Detection for Personal Mobility
Vehicles in Walking Space Using Dataset Generated by Simulation,
ICPR21(1788-1795)
IEEE DOI
2105
Legged locomotion, Space vehicles, Training, Solid modeling,
Laser radar, Wheelchairs
BibRef
John, V.[Vijay],
Boyali, A.[Ali],
Thompson, S.[Simon],
Lakshmanan, A.[Annamalai],
Mita, S.[Seiichi],
Visible and Thermal Camera-based Jaywalking Estimation Using a
Hierarchical Deep Learning Framework,
MMHUA20(123-135).
Springer DOI
2103
BibRef
Ujjwal, U.,
Dziri, A.,
Leroy, B.,
Bremond, F.,
A One-and-Half Stage Pedestrian Detector,
WACV20(765-774)
IEEE DOI
2006
Detectors, Proposals, Feature extraction, Semantics, Training,
Complexity theory, Autonomous vehicles
BibRef
Kress, V.[Viktor],
Zernetsch, S.[Stefan],
Doll, K.[Konrad],
Sick, B.[Bernhard],
Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent
Neural Networks,
3DHU20(57-71).
Springer DOI
2103
BibRef
Yuan, J.,
Wu, X.,
Yuan, S.,
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,
multi-scale information
BibRef
Sanjeewani, P.,
Verma, B.,
An optimisation Technique for the Detection of Safety Attributes
Using Roadside Video Data,
IVCNZ20(1-6)
IEEE DOI
2012
Industries, Evolutionary computation, Road safety, Safety,
Classification algorithms, Convolutional neural networks,
road safety
BibRef
Styles, O.,
Guha, T.,
Sanchez, V.,
Kot, A.C.,
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction
in a Network of Cameras,
Precognition20(4379-4382)
IEEE DOI
2008
Cameras, Trajectory, Task analysis, Forecasting, Databases,
Computational modeling, Object detection
BibRef
Habibi, G.,
Jaipuria, N.,
How, J.P.,
SILA: An Incremental Learning Approach for Pedestrian Trajectory
Prediction,
Precognition20(4411-4421)
IEEE DOI
2008
Trajectory, Hidden Markov models, Training, Prediction algorithms,
Data models, Predictive models, Encoding
BibRef
Fernando, T.,
Denman, S.,
Sridharan, S.,
Fookes, C.,
Neighbourhood Context Embeddings in Deep Inverse Reinforcement
Learning for Predicting Pedestrian Motion Over Long Time Horizons,
HBU19(1179-1187)
IEEE DOI
2004
behavioural sciences computing, entropy,
feature extraction, image motion analysis, pedestrians,
LSTM
BibRef
Chaabane, M.,
Trabelsi, A.,
Blanchard, N.,
Beveridge, R.,
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames
Prediction,
WACV20(2286-2295)
IEEE DOI
2006
Predictive models, Hidden Markov models, Feature extraction,
Autonomous vehicles, Decoding
BibRef
Zhang, P.[Pu],
Ouyang, W.L.[Wan-Li],
Zhang, P.F.[Peng-Fei],
Xue, J.R.[Jian-Ru],
Zheng, N.N.[Nan-Ning],
SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory
Prediction,
CVPR19(12077-12086).
IEEE DOI
2002
BibRef
Xue, H.[Hao],
Huynh, D.[Du],
Reynolds, M.[Mark],
Location-Velocity Attention for Pedestrian Trajectory Prediction,
WACV19(2038-2047)
IEEE DOI
1904
BibRef
Earlier:
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory
Prediction,
WACV18(1186-1194)
IEEE DOI
1806
BibRef
And:
Bi-Prediction: Pedestrian Trajectory Prediction Based on
Bidirectional LSTM Classification,
DICTA17(1-8)
IEEE DOI
1804
learning (artificial intelligence), pedestrians,
recurrent neural nets, pedestrian trajectory prediction,
Task analysis.
feature extraction, image recognition,
learning (artificial intelligence), neural nets,
Trajectory.
image classification, object detection.
BibRef
Dhaka, D.,
Ishii, M.,
Sato, A.,
Latent Linear Dynamics for Modeling Pedestrian Behaviors,
ICPR18(1592-1597)
IEEE DOI
1812
Trajectory, Data models, Clustering algorithms,
Inference algorithms, Dynamics, Heuristic algorithms, Kalman filters
BibRef
Barata, C.[Catarina],
Nascimento, J.C.[Jacinto C.],
Marques, J.S.[Jorge S.],
Improving a Switched Vector Field Model for Pedestrian Motion Analysis,
ACIVS18(3-13).
Springer DOI
1810
BibRef
Earlier:
A sparse approach to pedestrian trajectory modeling using multiple
motion fields,
ICIP17(2538-2542)
IEEE DOI
1803
Estimation, Hidden Markov models, Matching pursuit algorithms,
Optimization, Surveillance, Switches, Trajectory, Motion estimation,
sparse representation
BibRef
Yatim, H.S.M.[Halimatul Saadiah M.],
Talib, A.Z.[Abdullah Zawawi],
Haron, F.[Fazilah],
An Automated Image-Based Approach for Tracking Pedestrian Movements
from Top-View Video,
IVIC17(279-289).
Springer DOI
1711
BibRef
Gao, D.,
Wu, Z.,
Zhang, W.,
Safe-Net: Solid and Abstract Feature Extraction Network for
Pedestrian Attribute Recognition,
ICIP19(1655-1659)
IEEE DOI
1910
Pedestrian Attribute, GAN, Image Segmentation
BibRef
Suzuki, T.,
Aoki, Y.,
Kataoka, H.,
Pedestrian near-miss analysis on vehicle-mounted driving recorders,
MVA17(416-419)
DOI Link
1708
Autonomous vehicles, Benchmark testing,
Pattern recognition, Safety, Urban areas, Visualization
BibRef
Hasan, I.,
Setti, F.,
Tsesmelis, T.,
del Bue, A.,
Cristani, M.,
Galasso, F.,
'Seeing is Believing': Pedestrian Trajectory Forecasting Using Visual
Frustum of Attention,
WACV18(1178-1185)
IEEE DOI
1806
image motion analysis, minimisation, pedestrians, pose estimation,
collision avoidance, destination point, expected destination,
Visualization
BibRef
Ke, R.,
Lutin, J.,
Spears, J.,
Wang, Y.,
A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss
Detection Through Onboard Monocular Vision,
Traffic17(898-905)
IEEE DOI
1709
Cameras, Feature extraction, Safety, Sensors, Surveillance, Tracking, Videos
BibRef
Brazil, G.,
Yin, X.,
Liu, X.,
Illuminating Pedestrians via Simultaneous Detection and Segmentation,
ICCV17(4960-4969)
IEEE DOI
1802
feature extraction, image segmentation,
object detection, pedestrians, road safety,
BibRef
Cancela, B.,
Iglesias, A.,
Ortega, M.,
Penedo, M.G.,
Unsupervised Trajectory Modelling Using Temporal Information via
Minimal Paths,
CVPR14(2553-2560)
IEEE DOI
1409
geodesic active contours; pedestrian behavior; trajectory analysis
BibRef
Nakanishi, W.,
Fuse, T.,
Ishikawa, T.,
Adaptive Parameter Estimation of Person Recognition Model in a
Stochastic Human Tracking Process,
Seamless15(49-53).
DOI Link
1508
BibRef
Nakanishi, W.,
Fuse, T.,
Sensitive Analysis of Observation Model for Human Tracking Using a
Stochastic Process,
CloseRange14(445-450).
DOI Link
1411
BibRef
Earlier:
Multiple Human Tracking In Complex Situation By Data Assimilation With
Pedestrian Behavior Model,
ISPRS12(XXXIX-B3:409-414).
DOI Link
1209
BibRef
Leal-Taixe, L.[Laura],
Pons-Moll, G.[Gerard],
Rosenhahn, B.[Bodo],
Everybody needs somebody: Modeling social and grouping behavior on a
linear programming multiple people tracker,
MSVALC11(120-127).
IEEE DOI
1201
BibRef
Favoreel, W.[Wouter],
Pedestrian sensing for increased traffic safety and efficiency at
signalized intersections,
AVSBS11(539-542).
IEEE DOI
1111
AVSS 2011 demo session.
BibRef
Boltes, M.[Maik],
Zhang, J.[Jun],
Seyfried, A.[Armin],
Steffen, B.[Bernhard],
T-junction: Experiments, trajectory collection, and analysis,
MSVALC11(158-165).
IEEE DOI
1201
Pedestrian behavior at T-junctions.
BibRef
Ricci, E.[Elisa],
Tobia, F.[Francesco],
Zen, G.[Gloria],
Learning Pedestrian Trajectories with Kernels,
ICPR10(149-152).
IEEE DOI
1008
BibRef
Cho, H.G.[Hyung-Gi],
Rybski, P.[Paul],
Zhang, W.[Wende],
Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles,
CMU-RI-TR-10-11, January, 2010.
WWW Link.
1102
From the vehicle.
BibRef
Grubb, G.,
Zelinsky, A.,
Nilsson, L.,
Rilbe, M.,
3D vision sensing for improved pedestrian safety,
IVS04(19-24).
IEEE DOI
0411
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Detection, Tracking, Infrared, IR, Thermal Images .