16.7.4.2.11 Pedestrian Safety Issues, Pedestrian Behavior

Chapter Contents (Back)
Safety.
See also Crosswalk Detection, Zebra Crossings. Related to:
See also Driver Assistance Systems and Techniques.

Schlegel, C.[Christian], Illmann, J.[Jörg], Jaberg, H.[Heiko], Schuster, M.[Matthias], Wörz, R.[Robert],
Integrating Vision-Based Behaviors with an Autonomous Robot,
Videre(1), No. 4, Winter 2000, pp. xx-yy. 0005
BibRef
Earlier: CVS99(1 ff.).
Springer DOI 0209
BibRef
Earlier:
Vision Based Person Tracking with a Mobile Robot,
BMVC98(xx-yy). BibRef

Ling, H.[Huang], Wu, J.P.[Jian-Ping],
A study on cyclist behavior at signalized intersections,
ITS(5), No. 4, December 2004, pp. 293-299.
IEEE Abstract. 0501
BibRef

Jung, H.G., Kwak, B.M., Shim, J.S., Yoon, P J., Kim, J.,
Precrash Dipping Nose (PCDN) Needs Pedestrian Recognition,
ITS(9), No. 4, December 2008, pp. 678-687.
IEEE DOI 0812
BibRef

Sidla, O.[Oliver],
Improved pedestrian tracking for urban planning,
SPIE(Newsroom), December 17, 2009.
DOI Link 0912
Enhanced image-analysis methods enable new applications for public-transport scheduling, traffic control, and safety monitoring. BibRef

Treuillet, S.[Sylvie], Royer, E.[Eric],
Outdoor/indoor Vision-based Localization For Blind Pedestrian Navigation Assistance,
IJIG(10), No. 4, October 2010, pp. 481-496.
DOI Link 1101
BibRef

Greene, D., Liu, J., Reich, J., Hirokawa, Y., Shinagawa, A., Ito, H., Mikami, T.,
An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists,
ITS(12), No. 4, December 2011, pp. 942-953.
IEEE DOI 1112
BibRef

Keller, C.G., Dang, T., Fritz, H., Joos, A., Rabe, C., Gavrila, D.M.,
Active Pedestrian Safety by Automatic Braking and Evasive Steering,
ITS(12), No. 4, December 2011, pp. 1292-1304.
IEEE DOI 1112
BibRef

Pedestrians tracked for scheduling, traffic control, and safety,
VisSys(16), No. 2, February 2011.
HTML Version. News item refrencing SLR Engineering work.
See also SLR Engineering. BibRef 1102

Zhang, Y., Yao, D., Qiu, T.Z., Peng, L., Zhang, Y.,
Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data,
ITS(13), No. 4, December 2012, pp. 1832-1844.
IEEE DOI 1212
BibRef

Xu, Y., Xu, D., Lin, S., Han, T.X., Cao, X., Li, X.,
Detection of Sudden Pedestrian Crossings for Driving Assistance Systems,
SMC-B(42), No. 3, June 2012, pp. 729-739.
IEEE DOI 1202
BibRef

Kataoka, H.[Hirokatsu], Tamura, K.[Kimimasa], Iwata, K.[Kenji], Satoh, Y.[Yutaka], Matsui, Y.[Yasuhiro], Aoki, Y.[Yoshimitsu],
Extended Feature Descriptor and Vehicle Motion Model with Tracking-by-Detection for Pedestrian Active Safety,
IEICE(E97-D), No. 2, February 2013, pp. 296-304.
WWW Link. 1402
BibRef

Kataoka, H.[Hirokatsu], Hashimoto, K.[Kiyoshi], Iwata, K.[Kenji], Satoh, Y.[Yutaka], Navab, N.[Nassir], Ilic, S.[Slobodan], Aoki, Y.[Yoshimitsu],
Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition,
ACCV14(V: 336-349).
Springer DOI 1504
BibRef

Borges, P.V.K., Zlot, R., Tews, A.,
Integrating Off-Board Cameras and Vehicle On-Board Localization for Pedestrian Safety,
ITS(14), No. 2, 2013, pp. 720-730.
IEEE DOI 1307
Navigation; Safety; Tracking; Autonomous vehicles; pedestrian detection BibRef

Prioletti, A., Mogelmose, A., Grisleri, P., Trivedi, M.M., Broggi, A., Moeslund, T.B.,
Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation,
ITS(14), No. 3, 2013, pp. 1346-1359.
IEEE DOI 1309
Advanced driver assistance system (ADAS) BibRef

Zhang, Y., Yao, D., Qiu, T.Z., Peng, L.,
Scene-based pedestrian safety performance model in mixed traffic situation,
IET-ITS(8), No. 3, May 2014, pp. 209-218.
DOI Link 1407
BibRef

Vazquez, D.[David], Lopez, A.M.[Antonio M.], Marin, J., Ponsa, D.[Daniel], Geronimo, D.,
Virtual and Real World Adaptationfor Pedestrian Detection,
PAMI(36), No. 4, April 2014, pp. 797-809.
IEEE DOI 1404
BibRef
Earlier: A1, A2, A4, Only:
Unsupervised domain adaptation of virtual and real worlds for pedestrian detection,
ICPR12(3492-3495).
WWW Link. 1302
Accuracy BibRef

Geronimo, D.[David], Lopez, A.M.[Antonio M.],
Vision-based Pedestrian Protection Systems for Intelligent Vehicles,

Springer2014. ISBN 978-1-4614-7986-4.
WWW Link. 1404
BibRef

Harris, M.,
A cheaper way for robocars to avoid pedestrians,
Spectrum(52), No. 7, July 2015, pp. 16-16.
IEEE DOI 1507
BibRef

Pham, T.Q., Nakagawa, C., Shintani, A., Ito, T.,
Evaluation of the Effects of a Personal Mobility Vehicle on Multiple Pedestrians Using Personal Space,
ITS(16), No. 4, August 2015, pp. 2028-2037.
IEEE DOI 1508
Indexes BibRef

Said, Y., Atri, M.,
Efficient and high-performance pedestrian detector implementation for intelligent vehicles,
IET-ITS(10), No. 6, 2016, pp. 438-444.
DOI Link 1608
computer vision BibRef

Li, F.L.[Fu-Liang], Zhang, R.H.[Rong-Hui], You, F.[Feng],
Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment,
IET-IPR(11), No. 10, October 2017, pp. 833-840.
DOI Link 1710
BibRef

Jeong, M., Ko, B.C., Nam, J.Y.,
Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights,
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 Pedestrian Automatic Emergency Braking Systems,
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

Bhat, A., Aoki, S., Rajkumar, R.,
Tools and Methodologies for Autonomous Driving Systems,
PIEEE(106), No. 9, September 2018, pp. 1700-1716.
IEEE DOI 1810
automobiles, embedded systems, mobile robots, pedestrians, road safety, safety-critical software, verification, software tools BibRef

Strawderman, L.J., Campbell, B.S., May, D.C., Bethel, C.L., Usher, J.M., 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: Research Review and Roadmap for Future Research,
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 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 BibRef

Coscia, P.[Pasquale], Castaldo, F.[Francesco], Palmieri, F.A.N.[Francesco A.N.], Alahi, A.[Alexandre], Savarese, S.[Silvio], Ballan, L.[Lamberto],
Long-term path prediction in urban scenarios using circular distributions,
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., Carruth, D.W., Hudson, C.R.,
How Communicating Features can Help Pedestrian Safety in the Presence of Self-Driving Vehicles: Virtual Reality Experiment,
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 Means of Machine Learning,
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 for the whole indoor space without blind areas,
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], Mannini, L.[Livia], Valenti, G.[Gaetano], Zarelli, C.[Cristiano],
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 vehicle-pedestrian collisions at intersections,
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 Autonomous Driving Using Structural-LSTM Network,
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 for Pedestrian Detection,
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], Lam, S.K.[Siew-Kei],
Self-Growing Spatial Graph Networks for Pedestrian Trajectory Prediction,
WACV20(1140-1148)
IEEE DOI 2006
Trajectory, Predictive models, Logic gates, Task analysis, Dynamics, Data models, Adaptation models BibRef

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 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 pedestrian dynamics,
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
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
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


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
computer vision, 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, computer vision, 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
computer vision, 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 .


Last update:Nov 30, 2021 at 22:19:38