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- Title
VRSDNet: vehicle re-identification with a shortly and densely connected convolutional neural network.
- Authors
Zhu, Jianqing; Du, Yongzhao; Hu, Yang; Zheng, Lixin; Cai, Canhui
- Abstract
Vehicle re-identification aiming to match vehicle images captured by different cameras plays an important role in video surveillance for public security. In this paper, we solve Vehicle Re-identification with a Shortly and Densely connected convolutional neural Network (VRSDNet). The proposed VRSDNet mainly consists of a list of short and dense units (SDUs), necessary pooling and spatial normalization layers. Specifically, each SDU contains a short list of densely connected convolutional layers and each convolutional layer is of the same appropriate channels. As a result, the number of connections and the input channel of each convolutional layer are restricted in each SDU, and the architecture of VRSDNet is simple. Extensive experiments on both VeRi and VehicleID datasets show that the proposed VRSDNet is obviously superior to multiple state-of-the-art vehicle re-identification methods in terms of accuracy and speed.
- Subjects
VIDEO surveillance; GOVERNMENT securities; IMAGE registration; RETINAL blood vessels; VEHICLES
- Publication
Multimedia Tools & Applications, 2019, Vol 78, Issue 20, p29043
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-018-6270-4