Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 10; pp. 3459 - 3471
• Main Authors: Huang, Yan, Xu, Jingsong, Wu, Qiang, Zhong, Yi, Zhang, Peng, Zhang, Zhaoxiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Cloth; cloth change; Datasets; Face; Image acquisition; Internet; Lighting; long-term scenario; Neurons; Person re-identification; Security; Surveillance; vector-neuron capsules
• DOI: 10.1109/TCSVT.2019.2948093
• ISSN: 1051-8215

Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes. However, in the long-term re-ID scenario, a person has a great chance to change clothes. A sophisticated re-ID system should take such changes into account. To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called "Celeb-reID" to the community. Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset. Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities. There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far. To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network. Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person. We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task. Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting. Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets. Comprehensive analyses are given to demonstrate the challenge exposed in our datasets. Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario. The new dataset and code will be released to facilitate future researches.