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IEEE DOI
0601
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1504
Accidents
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Elsevier DOI
1609
BibRef
Earlier: A2, A4, A3, A1:
ZebraRecognizer: Efficient and Precise Localization of Pedestrian
Crossings,
ICPR14(2566-2571)
IEEE DOI
1412
Accelerometers
Visual impairment
See also Robust traffic lights detection on mobile devices for pedestrians with visual impairment.
BibRef
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1705
Automobiles, Ontologies, Real-time systems, Roads, Semantics, Sensors,
Advanced driver assistance system, anticipation, crossroads,
ontology, real-time decision making, safety, traffic, management
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Jiang, X.B.[Xiao-Bei],
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1710
BibRef
Völz, B.,
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Gilitschenski, I.,
Siegwart, R.,
Nieto, J.,
Inferring Pedestrian Motions at Urban Crosswalks,
ITS(20), No. 2, February 2019, pp. 544-555.
IEEE DOI
1902
Trajectory, Prediction algorithms, Automobiles, Roads, Safety,
Measurement, Task analysis, Autonomous vehicles, machine learning,
prediction methods
BibRef
Chen, A.T.,
Fan, J.,
Biglari-Abhari, M.,
Wang, K.I.,
A computationally efficient pipeline for camera-based indoor person
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IVCNZ17(1-6)
IEEE DOI
1902
feature extraction, image matching, object detection,
target tracking, unsupervised learning, video cameras,
Camera Surveillance
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Cao, Z.C.[Zheng-Cai],
Xu, X.W.[Xiao-Wen],
Hu, B.[Biao],
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Rapid Detection of Blind Roads and Crosswalks by Using a Lightweight
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2110
Convolution, Roads, Semantics, Feature extraction,
Image segmentation, Kernel, Machine learning,
deep convolutional network
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Qian, Y.Q.[Ye-Qiang],
Wang, C.X.[Chun-Xiang],
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IEEE DOI
2212
Predictive models, Data models, Computational modeling,
Pose estimation, Task analysis, Real-time systems, Convolution,
graph convolutional network
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Ni, R.R.[Rong-Rong],
Yang, B.[Biao],
Wei, Z.W.[Zhi-Wen],
Hu, H.Y.[Hong-Yu],
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Pedestrians crossing intention anticipation based on dual-channel
action recognition and hierarchical environmental context,
IET-ITS(17), No. 2, 2023, pp. 255-269.
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2302
BibRef
Zhou, Y.C.[Yu-Chen],
Tan, G.[Guang],
Zhong, R.[Rui],
Li, Y.[Yaokun],
Gou, C.[Chao],
PIT: Progressive Interaction Transformer for Pedestrian Crossing
Intention Prediction,
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IEEE DOI
2312
BibRef
Yang, B.[Biao],
Wei, Z.W.[Zhi-Wen],
Hu, H.Y.[Hong-Yu],
Wang, R.[Rui],
Yang, C.C.[Chang-Chun],
Ni, R.R.[Rong-Rong],
DPCIAN: A Novel Dual-Channel Pedestrian Crossing Intention
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WWW Link.
2406
Pedestrians, Skeleton, Feature extraction, Behavioral sciences,
Semantics, Fuses, Safety, Road safety,
scene object interaction
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Lu, X.Y.[Xing-Yuan],
Xue, Y.B.[Yan-Bing],
Wang, Z.G.[Zhi-Gang],
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X-CDNet: A real-time crosswalk detector based on YOLOX,
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2407
Crosswalk detection, CD9K, Reparameterization, X-CDNet
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Zhao, Z.F.[Zhen-Feng],
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Xiao, B.[Bo],
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2410
BibRef
Xu, R.S.[Run-Sheng],
Tafazzoli, F.[Faezeh],
Zhang, L.[Li],
Rehfeld, T.[Timo],
Krehl, G.[Gunther],
Seal, A.[Arunava],
Holistic Grid Fusion Based Stop Line Estimation,
ICPR21(8400-8407)
IEEE DOI
2105
Visualization, Roads, Sensor fusion,
Sensor phenomena and characterization, Feature extraction,
Online map validation
BibRef
Yu, S.[Samuel],
Lee, H.[Heon],
Kim, J.[John],
LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic
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Springer DOI
1909
BibRef
Liang, J.[Justin],
Urtasun, R.[Raquel],
End-to-End Deep Structured Models for Drawing Crosswalks,
ECCV18(XII: 407-423).
Springer DOI
1810
BibRef
Diaz, M.,
Girgis, R.,
Fevens, T.,
Cooperstock, J.,
To Veer or Not to Veer:
Learning from Experts How to Stay Within the Crosswalk,
ACVR17(1470-1479)
IEEE DOI
1802
Cameras, Gyroscopes, Mobile handsets, Sensors, Training, Urban areas
BibRef
Rasouli, A.,
Kotseruba, I.,
Tsotsos, J.K.,
Are They Going to Cross? A Benchmark Dataset and Baseline for
Pedestrian Crosswalk Behavior,
CVRoads17(206-213)
IEEE DOI
1802
Automobiles, Cameras, Meteorology, Roads, Trajectory, Videos
BibRef
Tosi, F.[Fabio],
Poggi, M.[Matteo],
Benincasa, A.[Antonio],
Mattoccia, S.[Stefano],
Beyond Local Reasoning for Stereo Confidence Estimation with Deep
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ECCV18(VI: 323-338).
Springer DOI
1810
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Verbin, D.[Dor],
Kiryati, N.[Nahum],
Crossing the Road Without Traffic Lights:
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CIAP17(II:534-544).
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1711
pedestrian safety.
BibRef
Perry, A.[Adi],
Kiryati, N.[Nahum],
Road-Crossing Assistance by Traffic Flow Analysis,
ACVR14(361-374).
Springer DOI
1504
BibRef
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Deep Stereo Fusion: Combining Multiple Disparity Hypotheses with
Deep-Learning,
3DV16(138-147)
IEEE DOI
1701
convolution
BibRef
Poggi, M.[Matteo],
Nanni, L.[Luca],
Mattoccia, S.[Stefano],
Crosswalk Recognition Through Point-Cloud Processing and Deep-Learning
Suited to a Wearable Mobility Aid for the Visually Impaired,
ISCA15(282-289).
Springer DOI
1511
BibRef
Arias, P.,
Riveiro, B.,
Soilán, M.,
Díaz-Vilariño, L.,
Martínez-Sánchez, J.,
Simple Approaches to Improve the Automatic Inventory of Zebra Crossing
from MLS Data,
CMRT15(103-108).
DOI Link
1602
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Gavrilovic, T.[Thomas],
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Frequency filtering and connected components characterization for
zebra-crossing and hatched markings detection,
PCVIA10(A:43).
PDF File.
1009
BibRef
Zhao, Q.,
Zhang, G.Y.,
Wood, R.L.,
Luo, Z.W.,
Video Based Real-Time Pedestrian Detection on Zebra Cross,
CISP09(1-4).
IEEE DOI
0910
BibRef
Soheilian, B.,
Paparoditis, N.,
Boldo, D.,
Rudant, J.P.,
3D zebra-crossing reconstruction from stereo rig images of a
ground-based mobile mapping system,
IEVM06(xx-yy).
PDF File.
0609
BibRef
Uddin, M.S.,
Shioyama, T.[Tadayoshi],
Bipolarity and Projective Invariant-Based Zebra-Crossing Detection for
the Visually Impaired,
VisImpaired05(III: 22-22).
IEEE DOI
0507
projective invarients to recognize crossing from candidates.
BibRef
Se, S.[Stephen],
Zebra-Crossing Detection for the Partially Sighted,
CVPR00(II: 211-217).
IEEE DOI
0005
Crosswalks
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Pedestrian Safety Issues, Pedestrian Behavior .