[1]ZHANG Guoyin,WANG Chuanbo,GAO Wei.Pedestrian multiobject tracking algorithm with anti-occlusion[J].CAAI Transactions on Intelligent Systems,2024,19(5):1248-1256.[doi:10.11992/tis.202307002]
Copy

Pedestrian multiobject tracking algorithm with anti-occlusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1248-1256 Column: 学术论文—智能系统 Public date: 2024-09-05
Title:
Pedestrian multiobject tracking algorithm with anti-occlusion
Author(s):
ZHANG Guoyin;  WANG Chuanbo;  GAO Wei
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Keywords:
computer vision; pedestrian tracking; target detection; re-identification; association algorithm; anti-occlusion; self-attention; feature extraction
CLC:
TP391
DOI:
10.11992/tis.202307002
Abstract:
A multiobject tracking algorithm with anti-occlusion (referred to as AOMOT), which is based on the FairMOT framework, is proposed to improve the accuracy of tracking systems in crowded pedestrian scenes. First, the lightweight balance module decouples semantic information at different levels to minimize semantic conflicts between detection and recognition tasks and decrease the impact of performance improvement in re-identification tasks. Second, the self-attention structure is adopted to extract pedestrian appearance features and improve the discrimination of intra-class features under local windows. The matching consistency of pedestrian identity information is enhanced, and frequent switching of identity signs is reduced. Finally, the identity association algorithm is optimized to mine occluded objects in low-confidence targets, reincorporate them into the target identity association, and update their recognition features. Experimental results show that, compared with the original model in the MOT17 dataset, the improved model enhances the higher-order tracking accuracy by 1.5 percentage points, improves identity F1 score by 3 percentage points, and reduces identity switching by 32%.
References:
[1] 魏旋旋, 黄子健, 曹乐, 等. 采用轻量级姿态估计网络的脊柱侧弯筛查方法[J]. 智能系统学报, 2023, 18(5): 1039-1046.
WEI Xuanxuan, HUANG Zijian, CAO Le, et al. Scoliosis screening method using lightweight pose estimation network[J]. CAAI transactions on intelligent systems, 2023, 18(5): 1039-1046.
[2] DAVID R, S?FFKER D. A review on machine learning-based models for lane-changing behavior prediction and recognition[J]. Frontiers in future transportation, 2023, 4: 950429.
[3] 高尚兵, 黄子赫, 耿璇, 等. 视觉协同的违规驾驶行为分析方法[J]. 智能系统学报, 2021, 16(6): 1158-1165.
GAO Shangbing, HUANG Zihe, GENG Xuan, et al. A visual collaborative analysis method for detecting illegal driving behavior[J]. CAAI transactions on intelligent systems, 2021, 16(6): 1158-1165.
[4] 胡硕, 王洁, 孙妍等. 无人机视角下的多车辆跟踪算法研究[J]. 智能系统学报, 2022, 17(4): 798-805.
HU Shuo, WANG Jie, SUN Yan, et al. Research on multi-vehicle tracking algorithm from the perspective of UAV[J]. CAAI transactions on intelligent systems, 2022, 17(4): 798-805.
[5] BASHAR M, ISLAM S, HUSSAIN K K, et al. Multiple object tracking in recent times: a literature review[EB/OL]. (2022-09-11)[2023-07-10]. https://arxiv.org/abs/2209.04796.
[6] ZHOU Xingyi, YIN Tianwei, KOLTUN V, et al. Global tracking transformers[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 8761-8770.
[7] SUN Peize, CAO Jinkun, JIANG Yi, et al. Transtrack: multiple object tracking with transformer[EB/OL]. (2020-12-01)[2023-07-10]. https://arxiv.org/abs/2012.15460.
[8] MEINHARDT T, KIRILLOV A, LEAL-TAIXé L, et al. TrackFormer: multi-object tracking with transformers[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 8834-8844.
[9] WU Jialian, CAO Jiale, SONG Liangchen, et al. Track to detect and segment: an online multi-object tracker[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 12347-12356.
[10] ZHOU Xingyi, KOLTUN V, KR?HENBüHL P. Tracking objects as points[C]//European Conference on Computer Vision. Cham: Springer, 2020: 474-490.
[11] HYUN J, KANG M, WEE D, et al. Detection recovery in online multi-object tracking with sparse graph tracker[C]//2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2023: 4839-4848.
[12] ZHANG Yifu, WANG Chunyu, WANG Xinggang, et al. FairMOT: on the fairness of detection and re-identification in multiple object tracking[J]. International journal of computer vision, 2021, 129(11): 3069-3087.
[13] DUAN Kaiwen, BAI Song, XIE Lingxi, et al. CenterNet: keypoint triplets for object detection[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 6568-6577.
[14] WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[C]//2017 IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 3645-3649.
[15] 王凯, 戴芳, 郭文艳, 等. 融合目标相似性和作用力的多目标跟踪[J]. 中国图象图形学报, 2024, 29(7): 1984-1997.
WANG Kai, DAI Fang, GUO Wenyan, et al. Integrating similarity and interaction force between objects for multiple object tracking[J]. Journal of image and graphics, 2024, 29(7): 1984-1997.
[16] YU En, LI Zhuoling, HAN Shoudong, et al. RelationTrack: relation-aware multiple object tracking with decoupled representation[J]. IEEE transactions on multimedia, 2023, 25: 2686-2697.
[17] 张英俊, 白小辉, 谢斌红. CNN-Transformer特征融合多目标跟踪算法[J]. 计算机工程与应用, 2024, 60(2): 180-190.
ZHANG Yingjun, BAI Xiaohui, XIE Binhong. Multi-object tracking algorithm based on CNN-transformer feature fusion[J]. Computer engineering and applications, 2024, 60(2): 180-190.
[18] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//European conference on computer vision. Cham: Springer, 2018: 3-19.
[19] LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 9992-10002.
[20] ZHANG Yifu, SUN Peize, JIANG Yi, et al. ByteTrack: multi-object tracking by associating every detection box[C]//Lecture Notes in Computer Science. Cham: Springer, 2022: 1-21.
[21] LAURA LEAL-TAIXé, MILAN A , REID I , et al. MOTChallenge 2015: towards a benchmark for multi-target tracking[EB/OL]. (2015-04-08) [2023-07-10]. https://arxiv.org/abs/1504.01942.
[22] MILAN A , LEAL-TAIXE L , REID I , et al. MOT16: a benchmark for multi-object tracking[EB/OL]. (2016-03-02)[2023-07-10]. https://arxiv.org/pdf/1603.00831.
[23] LUITEN J, OS?EP A, DENDORFER P, et al. HOTA: a higher order metric for evaluating multi-object tracking[J]. International journal of computer vision, 2021, 129(2): 548-578.
[24] SHAO Shuai, ZHAO Zijian, LI Boxun, et al. Crowdhuman: a benchmark for detecting human in a crowd[EB/OL]. (2018-04-30)[2023-07-10]. https://arxiv.org/abs/1805.00123.
[25] ESS A, LEIBE B, SCHINDLER K, et al. A mobile vision system for robust multi-person tracking[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage: IEEE, 2008: 1-8.
[26] ZHANG Shanshan, BENENSON R, SCHIELE B. CityPersons: a diverse dataset for pedestrian detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4457-4465.
[27] DOLLAR P, WOJEK C, SCHIELE B, et al. Pedestrian detection: a benchmark[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 304-311.
[28] XIAO Tong, LI Shuang, WANG Bochao, et al. Joint detection and identification feature learning for person search[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 3376-3385.
[29] ZHENG Liang, ZHANG Hengheng, SUN Shaoyan, et al. Person re-identification in the wild[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 3346-3355.
[30] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//European conference on computer vision. Cham: Springer, 2020: 213-229.
[31] XU Yihong, BAN Yutong, DELORME G, et al. TransCenter: transformers with dense representations for multiple-object tracking[J]. IEEE transactions on pattern analysis and machine intelligence, 2023, 45(6): 7820-7835.
[32] CAI Jiarui, XU Mingze, LI Wei, et al. MeMOT: multi-object tracking with memory[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 8080-8090.
[33] LIANG Chao, ZHANG Zhipeng, ZHOU Xue, et al. Rethinking the competition between detection and ReID in multiobject tracking[J]. IEEE transactions on image processing, 2022, 31: 3182-3196.
Similar References:

Memo

-

Last Update: 2024-09-05

Copyright © CAAI Transactions on Intelligent Systems