VeRI-Wild

A Large Dataset and a New Method for Vehicle Re-Identification in the Wild. A large-scale vehicle ReID dataset in the wild (VERI-Wild) is captured from a large CCTV surveillance system consisting of 174 cameras across one month (30*24h) under unconstrained scenarios. The cameras are distributed in a large urban district of more than 200km2. The YOLO-v2 [2] is used to detect the bounding box of vehicles. The raw vehicle image set contains 12 million vehicle images, and 11 volunteers are invited to clean the dataset for 1 month. After data cleaning and annotation, 416,314 vehicle images of 40,671 identities are collected. The statistics of VERI-Wild is illustrated in Figure. For privacy issues, the license plates are masked in the dataset. The distinctive features of VERI-Wild are summarized into the following aspects: Unconstrained capture conditions in the wild The VERI-Wild dataset is collected from a real CCTV camera system consisting of 174 surveillance cameras, in which the unconstrained image capture conditions pose a variety of challenges. Complex capture conditions The 174 surveillance cameras are distributed in an urban district over 200km2, presenting various backgrounds, resolutions, viewpoints, and occlusion in the wild. In extreme cases, one vehicle appears in more than 40 different cameras, which would be challenging for ReID algorithms. Large time span involving severe illumination and weather changes The VERI-Wild is collected from a duration of 125, 280 (174x24x30) video hours. gives the vehicle distributions in 4 time slots of 24h, i.e., morning, noon, afternoon, evening across 30 days. VERI-Wild also contains poor weather conditions, such as rainy, foggy, etc, which are not provided in previous datasets. Rich Context Information We provide rich context information such as camera IDs, timestamp, tracks relation across cameras, which are potential to facilitate the research on behavior analysis in camera networks, like vehicle behavior modeling, cross-camera tracking and graph-based retrieval.

《面向无约束野外场景的车辆重识别大规模数据集及新方法》
VERI-Wild是一款大规模野外车辆重识别（Vehicle ReID）数据集，其采集自一套包含174路摄像头的大型闭路电视监控系统，采集周期为一个月（30天×24小时），所有采集场景均处于无约束真实环境中。该监控系统的摄像头分布于面积超过200平方千米的大型城区内。研究采用YOLO-v2[2]算法检测车辆的边界框。原始车辆图像集共包含1200万张车辆图像，同时邀请11名志愿者耗时一个月完成数据集清洗工作。经数据清洗与标注流程后，最终共收集得到40671个身份类别的416314张车辆图像。VERI-Wild数据集的统计信息如图所示。出于隐私保护需求，数据集中的车牌均已做打码处理。
VERI-Wild数据集的显著特征可归纳为以下几个方面：
1. 无约束野外采集条件：VERI-Wild数据集采集自真实的174路监控摄像头系统，无约束的图像采集条件带来了多样化的识别挑战。
2. 复杂采集场景：174路监控摄像头覆盖200平方千米以上的城区区域，场景中存在多样的背景、分辨率、拍摄视角以及遮挡情况。在极端场景下，同一车辆会出现在40余台不同的摄像头中，这对车辆重识别算法提出了极高的挑战。
3. 超长采集周期，涵盖剧烈光照与天气变化：VERI-Wild的总采集时长为125280小时（174×24×30），并提供了30天内每日四个时段（早、午、下午、晚间）的车辆分布情况。此外，数据集还包含降雨、大雾等恶劣天气场景，这是此前同类数据集所未涵盖的内容。
4. 丰富的上下文信息：本数据集提供了摄像头ID、时间戳、跨摄像头跟踪轨迹等丰富的上下文信息，可用于推动监控网络中的行为分析研究，例如车辆行为建模、跨摄像头跟踪以及基于图结构的检索等任务。

搜集汇总

背景与挑战

背景概述

VeRI-Wild是一个大规模车辆重识别数据集，包含416,314张图像和40,671个车辆身份，采集自174个监控摄像头，覆盖多种复杂环境和天气条件。数据集提供了丰富的上下文信息，适用于车辆行为分析和跨摄像头追踪研究。