SIA OpenIR  > 机器人学研究室
Alternative TitleAdaptive Deep Multi-object Tracking Algorithm Fusing Crowd Density
刘金文1,2,3; 任卫红4; 田建东1,2
Source Publication模式识别与人工智能
Indexed ByEI
EI Accession number20212610549784
Contribution Rank1
Funding Organization国家自然科学基金项目(No.U2013210, 61821005)
Keyword多目标跟踪 人群密度图 行人重识别 三元组损失


Other Abstract

Multi-object tracking technology cannot well solve the problem of multi-object tracking in the scenarios with objects severely occluded, and therefore an adaptive deep multi-object tracking algorithm fusing crowd density is proposed. Firstly, the crowd density maps and object detection results are fused, and the location and the count information of crowd density maps are utilized to correct the detector results to eliminate missing and false detections. Then, adaptive triplet loss is employed to improve the loss function of the re-identification model and thus the discrimination of the algorithm for the re-identification feature is enhanced. Finally, final tracking results are obtained using the appearance and motion information for objects association. It is verified through the experiments that the proposed algorithm effectively solves the problem of multi-object tracking in severely occluded scenes.

Document Type期刊论文
Corresponding Author田建东
Affiliation1.中国科学院沈阳自动化研究所 机器人学研究室
3.中国科学院大学 计算机科学与技术学院
4.哈尔滨工业大学(深圳) 机电工程与自动化学院
Recommended Citation
GB/T 7714
刘金文,任卫红,田建东. 融合人群密度的自适应深度多目标跟踪算法[J]. 模式识别与人工智能,2021,34(5):385-397.
APA 刘金文,任卫红,&田建东.(2021).融合人群密度的自适应深度多目标跟踪算法.模式识别与人工智能,34(5),385-397.
MLA 刘金文,et al."融合人群密度的自适应深度多目标跟踪算法".模式识别与人工智能 34.5(2021):385-397.
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