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Open AccessArticle

Instance Hard Triplet Loss for In-video Person Re-identification

1
The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
2
Beijing Electro-mechanical Engineering Institute, Beijing 100074, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 2198; https://doi.org/10.3390/app10062198
Received: 7 March 2020 / Revised: 18 March 2020 / Accepted: 20 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology 2020)
Traditional Person Re-identification (ReID) methods mainly focus on cross-camera scenarios, while identifying a person in the same video/camera from adjacent subsequent frames is also an important question, for example, in human tracking and pose tracking. We try to address this unexplored in-video ReID problem with a new large-scale video-based ReID dataset called PoseTrack-ReID with full images available and a new network structure called ReID-Head, which can extract multi-person features efficiently in real time and can be integrated with both one-stage and two-stage human or pose detectors. A new loss function is also required to solve this new in-video problem. Hence, a triplet-based loss function with an online hard example mining designed to distinguish persons in the same video/group is proposed, called instance hard triplet loss, which can be applied in both cross-camera ReID and in-video ReID. Compared with the widely-used batch hard triplet loss, our proposed loss achieves competitive performance and saves more than 30% of the training time. We also propose an automatic reciprocal identity association method, so we can train our model in an unsupervised way, which further extends the potential applications of in-video ReID. The PoseTrack-ReID dataset and code will be publicly released. View Full-Text
Keywords: person ReID; video; triplet loss; pose; unsupervised learning person ReID; video; triplet loss; pose; unsupervised learning
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Fan, X.; Jiang, W.; Luo, H.; Mao, W.; Yu, H. Instance Hard Triplet Loss for In-video Person Re-identification. Appl. Sci. 2020, 10, 2198.

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