Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification
Researchers have introduced a new benchmark and dataset for Anytime Person Re-Identification (AT-ReID), designed to improve person retrieval across diverse conditions including varying times of day and long-term intervals. The AT-USTC dataset, collected over 21 months with 270 volunteers, offers a more extensive and varied dataset than previous efforts. To address the challenges of multi-scenario retrieval, a novel model called Uni-AT was proposed, featuring scenario-specific feature learning and strategies to manage inter-scenario interference and ensure balanced training. AI
IMPACT Enhances capabilities in surveillance and security systems by enabling more robust person identification across varied conditions.