EMD-based local matching for occluded person re-identification

Abstract

Person re-identification (Re-ID) is a vital computer vision task focused on matching images of a person of interest as they move across multiple non-overlapping cameras. Thanks to advancements in deep learning models, numerous important milestones have been achieved in the field of person Re-ID. Recent efforts have concentrated on addressing a more realistic scenario where pedestrians are partially occluded. This trend indicates a promising future for the practical implementation of person Re-ID systems. This paper builds upon our previous work, which successfully addressed single-shot person Re-ID using local matching information. For this task, Earth Mover’s Distance (EMD) is employed as a metric to measure similarity between two distributions. To handle multi-shot Re-ID, the proposed framework integrates a feature block, adapting the single-shot methodology to a multi-shot setting. Unlike conventional person Re-ID methods that employ a manually determined images of person, the proposed framework takes a query tracklet as input, which is automatically generated through human detection and tracking steps. To evaluate the proposed method, FAPR dataset (Fully Automated Person ReID) is used. This dataset is one of the few publicly available datasets built specifically for an end-to-end person Re-ID system. Various scenarios are rigorously examined to demonstrate the effectiveness of the proposed framework, especially in challenging conditions with strong occlusion. Across eight experimental scenarios, the proposed method achieves matching rates at rank-1 ranging from 76.3% to 100%. These results underscore the robustness and efficacy of our approach. Our source code is made available at: https://github.com/anhnhust/emd-person-reid.