Acta Optica Sinica, Volume. 41, Issue 15, 1515001(2021)

Occluded Pedestrian Detection Algorithm Based on Attention Mechanism

Ziyin Zou1,2, Shaoyan Gai1,2、*, Feipeng Da1,2,3, and Yu Li1,2
Author Affiliations
  • 1School of Automation, Southeast University, Nanjing, Jiangsu 210096, China
  • 2Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210096, China
  • 3Shenzhen Research Institute, Southeast University, Shenzhen, Guangdong 518063, China
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    References(29)

    [1] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).

    [2] Wu B, Nevatia R. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors[C]∥Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, October 17-21, 2005, Beijing, China., 90-97(2005).

    [3] Sabzmeydani P, Mori G. Detecting pedestrians by learning shapelet features[C]∥2007 IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, 2007, Minneapolis, MN, USA., 1-8(2007).

    [4] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

    [5] Girshick R. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1440-1448(2015).

    [6] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [7] [7] RedmonJ, DivvalaS, GirshickR, et al.You only look once: unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE Press, 2016: 779- 788.

    [8] [8] RedmonJ, FarhadiA. YOLO9000: better, faster, stronger[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 6517- 6525.

    [9] Redmon J. -04-08)[2019-09-22]. https:∥arxiv., org/abs/1804, 02767(2018).

    [10] Liu W, Anguelov D, Erhan D et al. SSD: single shot multiBox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9905, 21-37(2016).

    [11] Ju M R, Luo J N, Wang Z B et al. Multi-scale target detection algorithm based on attention mechanism[J]. Acta Optica Sinica, 40, 1315002(2020).

    [12] Zhao B, Wang C P, Fu Q et al. Multi-scale infrared pedestrian detection based on deep attention mechanism[J]. Acta Optica Sinica, 40, 0504001(2020).

    [13] [13] Zhang SS, BenensonR, SchieleB. CityPersons: a diverse dataset for pedestrian detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 4457- 4465.

    [14] Shao S, Zhao Z J, Li B X et al. -04-30)[2018-05-30]. https:∥arxiv., org/abs/1805, 00123(2018).

    [15] Zhang S F, Xie Y L, Wan J et al. WiderPerson: a diverse dataset for dense pedestrian detection in the wild[J]. IEEE Transactions on Multimedia, 22, 380-393(2020).

    [16] Zhang S F, Wen L Y, Bian X et al. Occlusion-aware R-CNN: detecting pedestrians in a crowd[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11207, 657-674(2018).

    [17] [17] Wang XL, Xiao TT, Jiang YN, et al.Repulsion loss: detecting pedestrians in a crowd[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 7774- 7783.

    [18] Bodla N, Singh B, Chellappa R et al. Soft-, 5562-5570(2017).

    [19] [19] Liu ST, HuangD, Wang YH. Adaptive NMS: refining pedestrian detection in a crowd[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE Press, 2019: 6452- 6461.

    [20] Fei C, Liu B, Chen Z et al. Learning pixel-level and instance-level context-aware features for pedestrian detection in crowds[J]. IEEE Access, 7, 94944-94953(2019).

    [21] Lin C Y, Xie H X, Zheng H. PedJointNet: joint head-shoulder and full body deep network for pedestrian detection[J]. IEEE Access, 7, 47687-47697(2019).

    [22] Wu J L, Zhou C L, Yang M et al. Temporal-context enhanced detection of heavily occluded pedestrians[C]∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA., 13427-13436(2020).

    [23] Hou Y Z, Zheng L, Gould S. Multiview detection with feature perspective transformation[M]. ∥Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science. Cham: Springer, 12352, 1-18(2020).

    [24] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

    [25] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 3-19(2018).

    [26] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [27] Zhang S S, Yang J, Schiele B. Occluded pedestrian detection through guided attention in CNNs[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 6995-7003(2018).

    [28] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).

    [29] Song T, Sun L Y, Xie D et al. Small-scale pedestrian detection based on topological line localization and temporal feature aggregation[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 554-569(2018).

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    Ziyin Zou, Shaoyan Gai, Feipeng Da, Yu Li. Occluded Pedestrian Detection Algorithm Based on Attention Mechanism[J]. Acta Optica Sinica, 2021, 41(15): 1515001

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    Paper Information

    Category: Machine Vision

    Received: Jan. 13, 2021

    Accepted: Mar. 8, 2021

    Published Online: Aug. 11, 2021

    The Author Email: Gai Shaoyan (qxxymm@163.com)

    DOI:10.3788/AOS202141.1515001

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