Related papers: Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification

Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification

URL: http://arxiv.org/abs/2011.06852v1
Date: Fri, 13 Nov 2020 10:50:21 GMT
Title: Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification
Authors: J. Tu, C. Chen, X. Huang, J. He and X. Guan
Abstract summary: Vehicle-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. We propose a feature representation with novel clues (DFR-ST) for vehicle re-ID. It is capable of building robust features in the embedding space by involving appearance and retemporal information.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Vehicle re-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. It is crucial for lots of applications such as security surveillance and traffic management. The remarkably similar appearances of distinct vehicles and the significant changes of viewpoints and illumination conditions take grand challenges to vehicle re-ID. Conventional solutions focus on designing global visual appearances without sufficient consideration of vehicles' spatiotamporal relationships in different images. In this paper, we propose a novel discriminative feature representation with spatiotemporal clues (DFR-ST) for vehicle re-ID. It is capable of building robust features in the embedding space by involving appearance and spatio-temporal information. Based on this multi-modal information, the proposed DFR-ST constructs an appearance model for a multi-grained visual representation by a two-stream architecture and a spatio-temporal metric to provide complementary information. Experimental results on two public datasets demonstrate DFR-ST outperforms the state-of-the-art methods, which validate the effectiveness of the proposed method.