Rev: A Video Engine for Object Re-identification at the City Scale
Tiantu Xu, Kaiwen Shen, Yang Lie Fu, Humphrey Shi, Felix Xiaozhu Lin · 2022
Object re-identification (Re ID) is a key application of city-scale cameras on the edge. It is challenged by the limited accuracy of vision algorithms and the large video volume. We present Rev, a practical ReID engine that builds upon three new techniques. (1) Rev formulates ReID as a spatiotemporal query. Instead of retrieving all the images of a target object, it looks for locations and times in which the target object appeared. (2) Rev makes robust assessment of the target object occurrences by clustering unreliable object features. Each resultant cluster represents the general impression of a distinct object. (3) Rev samples cameras strategically in order to maximize its spatiotemporal coverage at low compute cost. Through an evaluation on 25 hours of videos from 25 cameras, Rev reached a high accuracy of 0.87 (recall at 5) across 70 queries. It runs at 830 × of video realtime in achieving high accuracy.