DeepAI AI Chat
Log In Sign Up

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

08/21/2020
by   Haocong Rao, et al.
National University of Defense Technology
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
South China University of Technology International Student Union
Lanzhou University
NetEase, Inc
14

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations ("locality"), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20 Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/SGE-LA.

READ FULL TEXT
09/05/2020

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

Person re-identification (Re-ID) via gait features within 3D skeleton se...
07/05/2021

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

Person re-identification via 3D skeletons is an emerging topic with grea...
04/21/2022

SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification

Recent advances in skeleton-based person re-identification (re-ID) obtai...
03/29/2021

Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization

Cloth-Changing person re-identification (CC-ReID) aims at matching the s...