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 p
- PDF / 3,452,070 Bytes
- 16 Pages / 439.37 x 666.142 pts Page_size
- 63 Downloads / 227 Views
Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected] 2 Microsoft Research, Beijing 100080, China
Abstract. 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-todistant 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. Keywords: Vehicle re-identification · Progressive search ing · License plate verification · Spatiotemporal relation
1
· Deep learn-
Introduction
Vehicle, as a significant object class in urban video surveillance, attracts massive focuses in computer vision research field, such as detection [1], classification [2], and pose estimation [3]. However, vehicle re-identification (Re-Id) is still a frontier but important topic which is often neglected by researchers. The task of vehicle Re-Id is, given a probe vehicle image, to search in a database for images that contain the same vehicles captured by multiple cameras. Vehicle Re-Id has c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part II, LNCS 9906, pp. 869–884, 2016. DOI: 10.1007/978-3-319-46475-6 53
870
X. Liu et al.
Fig. 1. (a) Large intra-instance differences of the same vehicles from different views (left) and subtle inter-instance differences of similar vehicles (right). (b) The license plates for vehicle Re-Id. (Part of the plate is covered due to privacy.)
pervasive applications in video surveillance [4], intelligent transportation [5], and urban computing [6], which can quickly discover, locate, and track the target vehicles in large-scale surveillance videos. Different from vehicle detection, tracking or classification
Data Loading...
