Cloth-Changing Person Re-Identification with Invariant Feature Parsing for UAVs Applications

Recently, deep learning-based intelligent vehicle control systems have played an important role in real-time road conditions assessment applications. It relies primarily on unmanned aerial vehicles (UAVs) for specific target retrieval, especially Cloth-Changing Person Re-identification (CC-ReID) technology, to provide support for road observations and environmental monitoring. Existing CC-ReID methods mainly focus on the invariant features of the front and rear views that are independent of clothing; among them, global color enhancement is a commonly used strategy. However, this method usually reduces the chromatism between the target foreground and background, which can easily lead to the loss of features unrelated to clothing and reduce the model's performance. To solve this problem, this article proposes a data augmentation framework with Local Invariant Feature Transformation and Clothing Adversarial Parsing (LIFTCAP) for CC-ReID. The proposed framework is equipped with a Local Invariant Feature Transition (LIFT) module and a Clothes Adversarial Parsing (CAP) module. The former aims to extract invariant features for the same person with different clothes using the local transition manners. CAP is devoted to finding adversarial associations and parsing contour differences between clothing styles. Subsequently, a feature correlation strategy is alternately implemented between the two modules to complete the optimization procedure. Extensive experiments were conducted on the public CC-ReID datasets (LTCC and PRCC), demonstrating the superiority of our proposed method over the latest methods. Furthermore, our method achieved competitive performance, particularly on a surveillance video dataset (CCVID). In addition, based on the LIFTCAP strategy, the proposed algorithm can achieve a time efficiency as low as O(n) for detecting specific targets when deployed on a UAV server (Feisi X200) for real-time road conditions assessment and monitoring applications.