Vehicle Re-Identification: Pushing the Limits of Re-Identification
Abstract
In this paper, we present a series of techniques which help push the limits of vehicle re-identification. First, we establish a strong baseline by using one of the best person re-identification models and applying them to vehicle re-identification. Secondly, we show improvements in four key components of re-identification: 1) detection, 2) tracking, 3) model, 4) loss function. Finally, our improvements lead to the state-of-the-art in the vehicle re-identification dataset VeRi-776, with 85.20 mean Average Precision (mAP) and 96.60% Rank-1 accuracy. This represents a +17.65 mAP and +6.37 Rank-1 improvement over the literature.
Cite
Text
Ayala-Acevedo et al. "Vehicle Re-Identification: Pushing the Limits of Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Ayala-Acevedo et al. "Vehicle Re-Identification: Pushing the Limits of Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/ayalaacevedo2019cvprw-vehicle/)BibTeX
@inproceedings{ayalaacevedo2019cvprw-vehicle,
title = {{Vehicle Re-Identification: Pushing the Limits of Re-Identification}},
author = {Ayala-Acevedo, Abner and Devgun, Akash and Zahir, Sadri and Askary, Sid},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {291-296},
url = {https://mlanthology.org/cvprw/2019/ayalaacevedo2019cvprw-vehicle/}
}