Name
Title | ||
|---|---|---|
A Deep Learning-Based Approach To Progressive Vehicle Re-Identification For Urban Surveillance |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46475-6_53 | COMPUTER VISION - ECCV 2016, PT II |
Keywords | Field | DocType |
Vehicle re-identification, Progressive search, Deep learning, License plate verification, Spatiotemporal relation | Computer vision,Feature vector,High recurrence rate,Computer science,Filter (signal processing),Object Class,Exploit,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,License | Conference |
Volume | ISSN | Citations |
9906 | 0302-9743 | 42 |
PageRank | References | Authors |
1.46 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinchen Liu | 1 | 99 | 9.07 |
| Wu Liu | 2 | 275 | 34.53 |
| Tao Mei | 3 | 4702 | 288.54 |
| Huadong Ma | 4 | 2020 | 179.93 |