| [1] |
Ruan Z, Song C, Yang XH, Shen G, Liu Z. 2019. Empirical analysis of urban road traffic network: a case study in Hangzhou city, China. Physica A: Statistical Mechanics and Its Applications 527:121287 doi: 10.1016/j.physa.2019.121287 |
| [2] |
Zheng Z, Wang Z, Liu S, Ma W. 2024. Exploring the spatial effects on the level of congestion caused by traffic accidents in urban road networks: a case study of Beijing. Travel Behaviour and Society 35:100728 doi: 10.1016/j.tbs.2023.100728 |
| [3] |
Natapov A, Fisher-Gewirtzman D. 2016. Visibility of urban activities and pedestrian routes: an experiment in a virtual environment. Computers, Environment and Urban Systems 58:60−70 doi: 10.1016/j.compenvurbsys.2016.03.007 |
| [4] |
Gorrini A, Crociani L, Vizzari G, Bandini S. 2018. Observation results on pedestrian-vehicle interactions at non-signalized intersections towards simulation. Transportation Research Part F: Traffic Psychology and Behaviour 59:269−85 doi: 10.1016/j.trf.2018.09.016 |
| [5] |
Gerónimo D, López AM, Sappa AD, Graf T. 2010. Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 32:1239−58 doi: 10.1109/TPAMI.2009.122 |
| [6] |
Ayachi R, Afif M, Said Y, Abdelaali AB. 2020. Pedestrian detection for advanced driving assisting system: a transfer learning approach. 2020 5 th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, 2−5 September 2020. USA: IEEE. pp. 1−5. https://doi.org/10.1109/ATSIP49331.2020.9231559 |
| [7] |
Zhou W, Liu Y, Zhao L, Xu S, Wang C. 2023. Pedestrian crossing intention prediction from surveillance videos for over-the-horizon safety warning. IEEE Transactions on Intelligent Transportation Systems 25:1394−407 doi: 10.1109/TITS.2023.3314051 |
| [8] |
Ge J, Luo Y, Tei G. 2009. Real-time pedestrian detection and tracking at nighttime for driver-assistance systems. IEEE Transactions on Intelligent Transportation Systems 10:283−98 doi: 10.1109/TITS.2009.2018961 |
| [9] |
Byju J, Chitra R, Pranesh PE, Pavan RS, Aravinth J. 2021. Pedestrian detection and tracking in challenging conditions. 2021 7 th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 19−20 March 2021. USA: IEEE. pp. 399−403. https://doi.org/10.1109/ICACCS51430.2021.9441934 |
| [10] |
El Hamdani S, Benamar N, Younis M. 2020. Pedestrian support in intelligent transportation systems: challenges, solutions and open issues. Transportation Research Part C: Emerging Technologies 121:102856 doi: 10.1016/j.trc.2020.102856 |
| [11] |
Dalal N, Triggs B. 2005. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 20−25 June 2005. USA: IEEE. pp. 886−93. https://doi.org/10.1109/CVPR.2005.177 |
| [12] |
Yang Y, Xu K, Wang K. 2023. Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection. Frontiers in Physics 11:1121311 doi: 10.3389/fphy.2023.1121311 |
| [13] |
Ruan B, Zhang C. 2021. Occluded pedestrian detection combined with semantic features. IET Image Processing 15:2292−300 doi: 10.1049/ipr2.12196 |
| [14] |
Ding L, Wang Y, Laganière R, Huang D, Luo X, et al. 2021. A robust and fast multispectral pedestrian detection deep network. Knowledge-Based Systems 227:106990 doi: 10.1016/j.knosys.2021.106990 |
| [15] |
Zhou Y, Zeng X. 2024. Towards comprehensive understanding of pedestrians for autonomous driving: efficient multi-task-learning-based pedestrian detection, tracking and attribute recognition. Robotics and Autonomous Systems 171:104580 doi: 10.1016/j.robot.2023.104580 |
| [16] |
Ouyang W, Wang X. 2013. Joint deep learning for pedestrian detection. 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 1−8 December 2013. USA: IEEE. pp. 2056−63. https://doi.org/10.1109/ICCV.2013.257 |
| [17] |
Li J, Liang X, Shen S, Xu T, Feng J, et al. 2018. Scale-aware fast R-CNN for pedestrian detection. IEEE Transactions on Multimedia 20:985−96 doi: 10.1109/TMM.2017.2759508 |
| [18] |
Tian Y, Luo P, Wang X, Tang X. 2015. Deep learning strong parts for pedestrian detection. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7−13 December 2015. USA: IEEE. pp. 1904−12. https://doi.org/10.1109/ICCV.2015.221 |
| [19] |
Pavlakos G, Zhu L, Zhou X, Daniilidis K. 2018. Learning to estimate 3D human pose and shape from a single color image. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18−23 June 2018. USA: IEEE. pp. 459−68. https://doi.org/10.1109/CVPR.2018.00055 |
| [20] |
Li J, Wang C, Zhu H, Mao Y, Fang HS, et al. 2019. CrowdPose: efficient crowded scenes pose estimation and a new benchmark. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15−20 June 2019. USA: IEEE. pp. 10855−64. https://doi.org/10.1109/CVPR.2019.01112 |
| [21] |
Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, et al. 2016. DeepCut: joint subset partition and labeling for multi person pose estimation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27−30 June 2016. USA: IEEE. pp. 4929−37. https://doi.org/10.1109/CVPR.2016.533 |
| [22] |
Bulat A, Tzimiropoulos G. 2016. Human pose estimation via convolutional part heatmap regression. Computer Vision–ECCV 2016: 14 th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Part VII 14. Cham: Springer. pp. 717−32. https://doi.org/10.1007/978-3-319-46478-7_44 |
| [23] |
Yang D, Dai R, Wang Y, Mallick R, Minciullo L, et al. 2021. Selective spatio-temporal aggregation based pose refinement system: towards understanding human activities in real-world videos. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 3−8 January 2021. USA: IEEE. pp.2362−71. https://doi.org/10.1109/WACV48630.2021.00241 |
| [24] |
Liu S, Huang D, Wang Y. 2019. Adaptive NMS: refining pedestrian detection in a crowd. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA, 15−20 June 2019. USA: IEEE. pp. 6452−61. https://doi.org/10.1109/CVPR.2019.00662 |
| [25] |
Tang Y, Liu M, Li B, Wang Y, Ouyang W. 2023. OTP-NMS: toward optimal threshold prediction of NMS for crowded pedestrian detection. IEEE Transactions on Image Processing 32:3176−87 doi: 10.1109/TIP.2023.3273853 |
| [26] |
Husham Al-Badri A, Azman Ismail N, Al-Dulaimi K, Ahmed Salman G, Sah Hj Salam M. 2023. Adaptive Non-Maximum Suppression for improving performance of Rumex detection. Expert Systems with Applications 219:119634 doi: 10.1016/j.eswa.2023.119634 |
| [27] |
Gidaris S, Komodakis N. 2015. Object detection via a multi-region and semantic segmentation-aware CNN model. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7−13 December 2015. USA: IEEE. pp. 1134−42. https://doi.org/10.1109/ICCV.2015.135 |
| [28] |
Chen W, Zhu Y, Tian Z, Zhang F, Yao M. 2023. Occlusion and multi-scale pedestrian detection: A review. Array 19:100318 doi: 10.1016/j.array.2023.100318 |
| [29] |
Li F, Li X, Liu Q, Li Z. 2022. Occlusion handling and multi-scale pedestrian detection based on deep learning: a review. IEEE Access 10:19937−57 doi: 10.1109/ACCESS.2022.3150988 |
| [30] |
He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27−30 June 2016. USA: IEEE. pp. 770−78. https://doi.org/10.1109/CVPR.2016.90 |
| [31] |
Wang K, Wu Y, Ji Q. 2018. Head pose estimation on low-quality images. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, China, 15−19 May 2018. UAS: IEEE. pp. 540−47. https://doi.org/10.1109/FG.2018.00087 |
| [32] |
Chen J, Wu J, Richter K, Konrad J, Ishwar P. 2016. Estimating head pose orientation using extremely low resolution images. 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). Santa Fe, NM, USA, 27−30 June 2016. USA: IEEE. pp. 65−68. https://doi.org/10.1109/CVPR.2016.90 |
| [33] |
Ren S, He K, Girshick R, Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39:1137−49 doi: 10.1109/TPAMI.2016.2577031 |
| [34] |
Bodla N, Singh B, Chellappa R, Davis LS. 2017. Soft-NMS—improving object detection with one line of code. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22−29 October 2017. USA: IEEE. pp. 5562−70. https://doi.org/10.1109/ICCV.2017.593 |
| [35] |
Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, et al. 2019. Generalized intersection over union: a metric and a loss for bounding box regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15−20 June 2019. USA: IEEE. pp. 658−66. https://doi.org/10.1109/CVPR.2019.00075 |
| [36] |
Zhang S, Benenson R, Schiele B. 2017. CityPersons: a diverse dataset for pedestrian detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21−26 July 2017. USA: IEEE. pp. 4457−65. https://doi.org/10.1109/CVPR.2017.474 |
| [37] |
Du S, Pan W, Li N, Dai S, Xu B, et al. 2024. TSD-YOLO: small traffic sign detection based on improved YOLO v8. IET Image Processing doi: 10.1049/ipr2.13141 |
| [38] |
Yang Z, Gong Z, Zhang Q, Wang J. 2023. Analysis of pedestrian-related crossing behavior at intersections: a Latent Dirichlet Allocation approach. International Journal of Transportation Science and Technology 12:1052−63 doi: 10.1016/j.ijtst.2022.12.003 |
| [39] |
Liu W, Liao S, Hu W, Liang X, Chen X. 2018. Learning efficient single-stage pedestrian detectors by asymptotic localization fitting. Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Part XIV. Cham: Springer. pp. 643–59. https://doi.org/10.1007/978-3-030-01264-9_38 |
| [40] |
Hou L, Lu K, Yang X, Li Y, Xue J. 2023. G-rep: Gaussian representation for arbitrary-oriented object detection. Remote Sensing 15:757 doi: 10.3390/rs15030757 |
| [41] |
Xiao X, Feng X. 2023. Multi-object pedestrian tracking using improved YOLOv8 and OC-SORT. Sensors 23:8439 doi: 10.3390/s23208439 |
| [42] |
Zou T, Yang S, Zhang Y, Ye M. 2020. Attention guided neural network models for occluded pedestrian detection. Pattern Recognition Letters 131:91−97 doi: 10.1016/j.patrec.2019.12.010 |