# Spatial-temporal Vehicle Re-identification

arXiv: https://arxiv.org/abs/2309.01166 · HTML: https://mlsanity.com/paper/2309.01166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [Pose-driven Deep Convolutional Model for Person Re-identification](https://mlsanity.com/paper/1709.08325/) (2017)
- [Parsing-based View-aware Embedding Network for Vehicle Re-Identification](https://mlsanity.com/paper/2004.05021/) (2020)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification](https://mlsanity.com/paper/2004.06305/) (2020)
- [The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification](https://mlsanity.com/paper/2004.06271/) (2020)
- [A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification](https://mlsanity.com/paper/1905.03397/) (2019)
- [PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data](https://mlsanity.com/paper/2005.00673/) (2020)
- [Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network](https://mlsanity.com/paper/2008.11423/) (2020)
- [Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals](https://mlsanity.com/paper/1708.03918/) (2017)
- [Multi-Domain Learning and Identity Mining for Vehicle Re-Identification](https://mlsanity.com/paper/2004.10547/) (2020)
- [A Strong Baseline for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10850/) (2021)
- [Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems](https://mlsanity.com/paper/2110.07933/) (2021)
- [Learning Instance-level Spatial-Temporal Patterns for Person Re-identification](https://mlsanity.com/paper/2108.00171/) (2021)
- [Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras](https://mlsanity.com/paper/1710.00983/) (2017)

## Built on by (most cited first, 1 of 1)

- [Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matching](https://mlsanity.com/paper/2408.05558/) (2024)

## Abstract (arXiv)

Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle across multiple cameras. To overcome the challenge, we propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology based on the adaptive Parzen window method and optimally combines the appearance and spatial-temporal similarities through the fusion network. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy. The experimental results support that utilizing spatial and temporal information for ReID can leverage the accuracy of appearance-based methods and effectively deal with appearance ambiguities.