COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
This addresses a real-world limitation in person re-identification for security and surveillance applications, but it is incremental as it builds on existing re-id methods with a new dataset and setting.
The paper tackles the problem of person re-identification when individuals change clothes, constructing a new large-scale dataset (COCAS) with 62,382 images from 5,266 persons and proposing a two-branch network (BC-Net) that integrates biometric and clothes features, showing feasibility with clothes templates.
Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates.