期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3538490
关键词
Person re-identification; two streams; optimized region matching; 3D skeleton
This paper proposes a person re-identification method based on 3D skeleton and two-stream approach, which can solve the problems in person re-identification and outperforms existing methods in recognition accuracy according to experimental results.
Person re-identification (Re-ID) is a challenging and arduous task due to non-overlapping views, complex background, and uncontrollable occlusion in video surveillance. An existing method for capturing pedestrian local region information is to divide person regions into horizontal stripes, which may lead to invalid features and erroneous learning. To solve this problem, this paper proposes a 3D skeleton and a two-stream approach to person Re-ID. The first stream of the method uses the 3D skeleton for background filtering and region segmentation. The second stream uses Siamese net to extract the global descriptor. The features of the two streams are fused to preserve the integrity of the person. An optimized region matching method for metric learning is designed. Extensive comparing experiments were conducted with state-of-the-art Re-ID methods on the Market-1501, CUHK03, and DukeMTMC-reID datasets. Experimental results show that the proposed method outperforms the existing methods in recognition accuracy.
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