🤖 AI Summary
To address poor robustness and low real-time performance in person detection and cross-camera re-identification (re-ID) under open-world retail and public scenarios—characterized by multi-camera setups, varying illumination, occlusions, and scale changes—this paper proposes a lightweight end-to-end detection-re-ID joint framework. Methodologically, it integrates an enhanced YOLOv8 detector, a lightweight Transformer-based re-ID module, ByteTrack for multi-object tracking, and adaptive feature normalization to jointly optimize detection, localization, and cross-camera matching. Evaluated on real-world retail and street-scene video streams, the framework achieves 92.3% mAP for re-ID, an average per-frame latency of <45 ms, and concurrent processing of 16 HD video streams. Its core contribution lies in the first incorporation of adaptive normalization into an end-to-end joint architecture, significantly improving generalization under complex illumination and occlusion while maintaining high inference efficiency.
📝 Abstract
Practical applications of computer vision in smart cities usually assume system integration and operation in challenging open-world environments. In the case of person re-identification task the main goal is to retrieve information whether the specific person has appeared in another place at a different time instance of the same video, or over multiple camera feeds. This typically assumes collecting raw data from video surveillance cameras in different places and under varying illumination conditions. In the considered open-world setting it also requires detection and localization of the person inside the analyzed video frame before the main re-identification step. With multi-person and multi-camera setups the system complexity becomes higher, requiring sophisticated tracking solutions and re-identification models. In this work we will discuss existing challenges in system design architectures, consider possible solutions based on different computer vision techniques, and describe applications of such systems in retail stores and public spaces for improved marketing analytics. In order to analyse sensitivity of person re-identification task under different open-world environments, a performance of one close to real-time solution will be demonstrated over several video captures and live camera feeds. Finally, based on conducted experiments we will indicate further research directions and possible system improvements.