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

TL;DR

This paper introduces a novel vehicle re-identification method that combines appearance features with spatio-temporal cues to improve matching accuracy across different camera views.

Contribution

It proposes a discriminative feature representation with a two-stream architecture and spatio-temporal metric, integrating multi-modal information for robust vehicle re-ID.

Findings

Outperforms state-of-the-art methods on public datasets

Effectively handles viewpoint and illumination variations

Demonstrates robustness with multi-modal feature integration

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.