Person Re-Identification by Discriminative Selection in Video Ranking
This work addresses person re-identification for surveillance systems by improving accuracy through video fragment selection, though it is incremental as it builds on existing multi-shot methods.
The paper tackles the problem of person re-identification in surveillance by addressing the limitations of single-frame methods, proposing a model that selects discriminative video fragments and learns a ranking function, resulting in improved performance over state-of-the-art methods on datasets like PRID2011, iLIDS-VID, and HDA+.
Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID$2011$, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods.