Cloth-Changing person re-identification (CC-ReID) aims at matching the same
person across different locations over a long-duration, e.g., over days, and
therefore inevitably meets challenge of changing clothing. In this paper, we
focus on handling well the CC-ReID problem under a more challenging setting,
i.e., just from a single image, which enables high-efficiency and latency-free
pedestrian identify for real-time surveillance applications. Specifically, we
introduce Gait recognition as an auxiliary task to drive the Image ReID model
to learn cloth-agnostic representations by leveraging personal unique and
cloth-independent gait information, we name this framework as GI-ReID. GI-ReID
adopts a two-stream architecture that consists of a image ReID-Stream and an
auxiliary gait recognition stream (Gait-Stream). The Gait-Stream, that is
discarded in the inference for high computational efficiency, acts as a
regulator to encourage the ReID-Stream to capture cloth-invariant biometric
motion features during the training. To get temporal continuous motion cues
from a single image, we design a Gait Sequence Prediction (GSP) module for
Gait-Stream to enrich gait information. Finally, a high-level semantics
consistency over two streams is enforced for effective knowledge
regularization. Experiments on multiple image-based Cloth-Changing ReID
benchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID
performs favorably against the state-of-the-arts. Codes are available at
this https URL

本文提出一种名为 GI-ReID 的框架，利用人体独特的不依赖于衣服的步态信息作为辅助任务，从单张图像识别行人并实现实时监控，并在衣服变化的 ReID 标准测试集上得到了显著的性能提升。