Advanced Search
Volume 45 Issue 5
May  2023
Turn off MathJax
Article Contents
XIE Minghong, KANG Bin, LI Huafeng, ZHANG Yafei. Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1833-1841. doi: 10.11999/JEIT220444
Citation: XIE Minghong, KANG Bin, LI Huafeng, ZHANG Yafei. Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1833-1841. doi: 10.11999/JEIT220444

Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm

doi: 10.11999/JEIT220444
  • Received Date: 2022-04-14
  • Accepted Date: 2022-09-06
  • Rev Recd Date: 2022-08-31
  • Available Online: 2022-09-08
  • Publish Date: 2023-05-10
  • Due to its relatively higher accuracy, the Anchor base algorithm has become a research hotspot for pedestrian detection in crowded scenes. However, the algorithm needs to design manually anchor boxes, which limits its generality. At the same time, a single Non-Maximum Suppression (NMS) screening threshold acting on crowd areas with different densities will lead to a certain degree of missed detection or false detection. To this end, a dual-head detection algorithm combining Anchor free and Anchor base detectors is proposed. Specifically, the Anchor free detector is used to perform rough detection on the image, and the coarse detection results are automatically clustered to generate anchor frames and then fed back to the Region Proposal Network (RPN) module, instead of manually designing the anchor frames in the RPN stage. Meanwhile, the density information of the population in different regions can be obtained through the statistics of the rough detection result information. A pedestrian head-whole body mutual supervision detection framework is designed, and the head detection results and the whole body detection results supervise each other, so as to reduce effectively the suppressed and missed target instances. A novel NMS method is proposed, which can adaptively select appropriate screening thresholds for crowd regions of different densities, thereby minimizing false detections caused by NMS process. The proposed detector is experimentally validated on the CrowdHuman dataset and the CityPersons dataset, achieving comparable performance to current state-of-the-art pedestrian detection methods.
  • loading
  • [1]
    YE Mang, SHEN Jianbing, LIN Gaojie, et al. Deep learning for person Re-identification: A survey and outlook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 2872–2893. doi: 10.1109/TPAMI.2021.3054775
    [2]
    MARVASTI-ZADEH S M, CHENG Li, GHANEI-YAKHDAN H, et al. Deep learning for visual tracking: A comprehensive survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(5): 3943–3968. doi: 10.1109/TITS.2020.3046478
    [3]
    贲晛烨, 徐森, 王科俊. 行人步态的特征表达及识别综述[J]. 模式识别与人工智能, 2012, 25(1): 71–81. doi: 10.3969/j.issn.1003-6059.2012.01.010

    BEN Xianye, XU Sen, and WANG Kejun. Review on pedestrian gait feature expression and recognition[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(1): 71–81. doi: 10.3969/j.issn.1003-6059.2012.01.010
    [4]
    邹逸群, 肖志红, 唐夏菲, 等. Anchor-free的尺度自适应行人检测算法[J]. 控制与决策, 2021, 36(2): 295–302. doi: 10.13195/j.kzyjc.2020.0124

    ZOU Yiqun, XIAO Zhihong, TANG Xiafei, et al. Anchor-free scale adaptive pedestrian detection algorithm[J]. Control and Decision, 2021, 36(2): 295–302. doi: 10.13195/j.kzyjc.2020.0124
    [5]
    ZHOU Chunluan and YUAN Junsong. Bi-box regression for pedestrian detection and occlusion estimation[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 135–151.
    [6]
    WANG Xinlong, XIAO Tete, JIANG Yuning, et al. Repulsion loss: Detecting pedestrians in a crowd[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7774–7783.
    [7]
    CHI Cheng, ZHANG Shifeng, XING Junliang, et al. Relational learning for joint head and human detection[C]. The Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA, 2020: 10647–10654.
    [8]
    陈勇, 谢文阳, 刘焕淋, 等. 结合头部和整体信息的多特征融合行人检测[J]. 电子与信息学报, 2022, 44(4): 1453–1460. doi: 10.11999/JEIT210268

    CHEN Yong, XIE Wenyang, LIU Huanlin, et al. Multi-feature fusion pedestrian detection combining head and overall information[J]. Journal of Electronics& Information Technology, 2022, 44(4): 1453–1460. doi: 10.11999/JEIT210268
    [9]
    LIU Songtao, HUANG Di, and WANG Yunhong. Adaptive NMS: Refining pedestrian detection in a crowd[C]. The 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 6452–6461.
    [10]
    HUANG Xin, GE Zheng, JIE Zequn, et al. NMS by representative region: Towards crowded pedestrian detection by proposal pairing[C]. The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 10747–10756.
    [11]
    REN Shaoqing, HE Kaiming, 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, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031
    [12]
    ZHOU Xingyi, WANG Dequan, and KRÄHENBÜHL P. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850, 2019.
    [13]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318–327. doi: 10.1109/TPAMI.2018.2858826
    [14]
    ZHENG Zhaohui, WANG Ping, REN Dongwei, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 8574–8586. doi: 10.1109/TCYB.2021.3095305
    [15]
    BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS--improving object detection with one line of code[C]. The 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 5562–5570.
    [16]
    SHAO Shuai, ZHAO Zijian, LI Boxun, et al. CrowdHuman: A benchmark for detecting human in a crowd[EB/OL]. https://arxiv.org/abs/1805.00123, 2018.
    [17]
    ZHANG Shanshan, BENENSON R, and SCHIELE B. CityPersons: A diverse dataset for pedestrian detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017, 4457–4465.
    [18]
    SHAO Xiaotao, WANG Qing, YANG Wei, et al. Multi-scale feature pyramid network: A heavily occluded pedestrian detection network based on ResNet[J]. Sensors, 2021, 21(5): 1820. doi: 10.3390/s21051820
    [19]
    LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. The 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 936–944.
    [20]
    ZHANG Shifeng, WEN Longyin, BIAN Xiaobian, et al. Occlusion-aware R-CNN: Detecting pedestrians in a crowd[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 657–674.
    [21]
    PANG Yanwei, XIE Jin, KHAN M H, et al. Mask-guided attention network for occluded pedestrian detection[C]. The 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 4966–4974.
    [22]
    LIN Zebin, PEI Wenjie, CHEN Fanglin, et al. Pedestrian detection by exemplar-guided contrastive learning[J]. IEEE Transactions on Image Processing, 2023, 32: 2003–2016. doi: 10.1109/TIP.2022.3189803
    [23]
    CHU Xuangeng, ZHENG Anlin, ZHANG Xiangyu, et al. Detection in crowded scenes: One proposal, multiple predictions[C]. The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 12211–12220.
    [24]
    陈勇, 刘曦, 刘焕淋. 基于特征通道和空间联合注意机制的遮挡行人检测方法[J]. 电子与信息学报, 2020, 42(6): 1486–1493. doi: 10.11999/JEIT190606

    CHEN Yong, LIU Xi, and LIU Huanlin. Occluded pedestrian detection based on joint attention mechanism of channel-wise and spatial information[J]. Journal of Electronics& Information Technology, 2020, 42(6): 1486–1493. doi: 10.11999/JEIT190606
    [25]
    ZHOU Penghao, ZHOU Chong, PENG Pai, et al. NOH-NMS: Improving pedestrian detection by nearby objects hallucination[C]. The 28th ACM International Conference on Multimedia, Seattle, USA, 2020: 1967–1975.
    [26]
    LIN M, LI Chuming, BU Xingyuan, et al. DETR for crowd pedestrian detection[EB/OL]. https://arxiv.org/abs/2012.06785, 2020.
    [27]
    SHANG Mingyang, XIANG Dawei, WANG Zhicheng, et al. V2F-Net: Explicit decomposition of occluded pedestrian detection[EB/OL]. https://arxiv.org/abs/2104.03106, 2021.
    [28]
    LI Zeming, PENG Chao, YU Gang, et al. DetNet: A backbone network for object detection[EB/OL]. https://arxiv.org/abs/1804.06215, 2018.
    [29]
    CAI Zhaowei and VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6154–6162.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(9)

    Article Metrics

    Article views (504) PDF downloads(120) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return