VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild

By Yihang Lou, Yan Bai, Jun Liu, Shiqi Wang, Ling-yu Duan. Computer Vision and Pattern Recognition, 2019

304 citations recorded by the source corpus.

Vehicle Re-identification (ReID) is of great significance to the intelligent transportation and public security. However, many challenging issues of Vehicle ReID in real-world scenarios have not been fully investigated, e.g., the high viewpoint variations, extreme illumination conditions, complex backgrounds, and different camera sources. To promote the research of vehicle ReID in the wild, we collect a new dataset called VERI-Wild with the following distinct features: 1) The vehicle images are captured by a large surveillance system containing 174 cameras covering a large urban district (more than 200km^2) The camera network continuously captures vehicles for 24 hours in each day and lasts for 1 month. 3) It is the first vehicle ReID dataset that is collected from unconstrained conditionsns. It is also a large dataset containing more than 400 thousand images of 40 thousand vehicle IDs. In this paper, we also propose a new method for vehicle ReID, in which, the ReID model is coupled into a Feature Distance Adversarial Network (FDA-Net), and a novel feature distance adversary scheme is designed to generate hard negative samples in feature space to facilitate ReID model training. The comprehensive results show the effectiveness of our method on the proposed dataset and the other two existing datasets.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/85a2ba580f6faa73f38672b0517cbcdeb84cf6c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment (2022, background): “On the other hand, for the sake of security, LQ images sometimes impede the normal running of autonomous vehicles [5, 23].”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, unknown): “VERI-Wild [139] 2019 ✓ ✓ ✓ ✓ 125, 280 video hours China Research Only”
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency (2021, background): “On the other hand, at one extreme, not being able to assess the image quality accurately can be life-threatening ( e.g., when low-quality images impede the ability of autonomous vehicles [1, 2] and traffic controllers [3] to safely navigate environments).”
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification (2020, background): “Through extensive experimentation in section 4, we show the effectiveness of our approach on multiple challenging vehicle re-id benchmarks [43, 22, 9, 27, 24], obtaining state-of-the-art results.”
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification (2021, unknown): “3 FDA-NeT [34] 84.”