A Deep Learning Based Approach to Progressive Vehicle Re identification for Urban Surveillance
While re identification (Re Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g. license plate) In this paper, we propose a novel deep learning based approach to PROgressive Vehicle re ID, called "PROVID" Our approach treats vehicle Re Id as two specific progressive search processes: coarse to fine search in the feature space, and near to distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near to distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re Id research, we collect to date the largest dataset named VeRi 776 from large scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi 776 shows that our approach outperforms the state of the art methods by 9.28 improvements in term of mAP.
ECCV