Self-supervised human semantic parsing for video-based person re-identification

Video-based person re-identification is an important research topic in computer vision that entails associating a pedestrian’s identity with non-overlapping cameras. It suffers from severe temporal appearance misalignment and visual ambiguity problems. We propose a novel self-supervised human semantic parsing approach (SS-HSP) for video-based person re-identification in this work. It employs self-supervised learning to adaptively segment the human body at pixel-level by estimating motion information of each body part between consecutive frames and explores complementary temporal relations for pursuing reinforced appearance and motion representations. Specifically, a semantic segmentation network within SS-HSP is designed, which exploits self-supervised learning by constructing a pretext task of predicting future frames. The network learns precise human semantic parsing together with the motion field of each body part between consecutive frames, which permits the reconstruction of future frames with the aid of several customized loss functions. Local aligned features of body parts are obtained according to the estimated human parsing. Moreover, an aggregation network is proposed to explore the correlation information across video frames for refining the appearance and motion representations. Extensive experiments on two video datasets have demonstrated the effectiveness of the proposed approach.