2020/12/17 by Tiantu Xu, Xu, Tiantu, Kaiwen Shen +7
Computer Science · Engineering · Environmental Science · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #Databases (cs.DB) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2012.09329
openalex publication_date 2020/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal queries for locations and times in which the target object appeared. Spatiotemporal reID is challenged by the accuracy limitation in computer vision algorithms and the colossal videos from city cameras. We present Clique, a practical ReID engine that builds upon two new techniques: (1) Clique assesses target occurrences by clustering fuzzy object features extracted by ReID algorithms, with each cluster representing the general impression of a distinct object to be matched against the input; (2) to search in videos, Clique samples cameras to maximize the spatiotemporal coverage and incrementally adds cameras for processing on demand. Through evaluation on 25 hours of videos from 25 cameras, Clique reached a high accuracy of 0.87 (recall at 5) across 70 queries and runs at 830x of video realtime in achieving high accuracy.