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DOI Link
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Enhanced image-analysis methods enable new applications for
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Springer DOI
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Borges, P.V.K.,
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Integrating Off-Board Cameras and Vehicle On-Board Localization
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ITS(14), No. 2, 2013, pp. 720-730.
IEEE DOI
1307
Navigation; Safety; Tracking; Autonomous vehicles; pedestrian detection
BibRef
Prioletti, A.,
Mogelmose, A.,
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Part-Based Pedestrian Detection and Feature-Based Tracking for Driver
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ITS(14), No. 3, 2013, pp. 1346-1359.
IEEE DOI
1309
Advanced driver assistance system (ADAS)
BibRef
Zhang, Y.,
Yao, D.,
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Peng, L.,
Scene-based pedestrian safety performance model in mixed traffic
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IET-ITS(8), No. 3, May 2014, pp. 209-218.
DOI Link
1407
BibRef
Harris, M.,
A cheaper way for robocars to avoid pedestrians,
Spectrum(52), No. 7, July 2015, pp. 16-16.
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1507
BibRef
Pham, T.Q.,
Nakagawa, C.,
Shintani, A.,
Ito, T.,
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
Li, F.L.[Fu-Liang],
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Fast pedestrian detection and dynamic tracking for intelligent vehicles
within V2V cooperative environment,
IET-IPR(11), No. 10, October 2017, pp. 833-840.
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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
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
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
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
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
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
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, Computer vision,
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
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
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 .