RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking

• URL: http://arxiv.org/abs/2507.08729v2
• Date: Mon, 21 Jul 2025 11:26:42 GMT
• Title: RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking
• Authors: Yuqiang Lin, Sam Lockyer, Mingxuan Sui, Li Gan, Florian Stanek, Markus Zarbock, Wenbin Li, Adrian Evans, Nic Zhang,
• Abstract summary: RoundaboutHD is a high-resolution multi-camera vehicle tracking benchmark dataset.<n>It offers temporal consistency video footage and enhanced challenges.<n>We provide baseline results for vehicle detection, single-camera tracking, image-based vehicle re-identification, and multi-camera tracking.
• Score: 2.1947594726633324
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The multi-camera vehicle tracking (MCVT) framework holds significant potential for smart city applications, including anomaly detection, traffic density estimation, and suspect vehicle tracking. However, current publicly available datasets exhibit limitations, such as overly simplistic scenarios, low-resolution footage, and insufficiently diverse conditions, creating a considerable gap between academic research and real-world scenario. To fill this gap, we introduce RoundaboutHD, a comprehensive, high-resolution multi-camera vehicle tracking benchmark dataset specifically designed to represent real-world roundabout scenarios. RoundaboutHD provides a total of 40 minutes of labelled video footage captured by four non-overlapping, high-resolution (4K resolution, 15 fps) cameras. In total, 512 unique vehicle identities are annotated across different camera views, offering rich cross-camera association data. RoundaboutHD offers temporal consistency video footage and enhanced challenges, including increased occlusions and nonlinear movement inside the roundabout. In addition to the full MCVT dataset, several subsets are also available for object detection, single camera tracking, and image-based vehicle re-identification (ReID) tasks. Vehicle model information and camera modelling/ geometry information are also included to support further analysis. We provide baseline results for vehicle detection, single-camera tracking, image-based vehicle re-identification, and multi-camera tracking. The dataset and the evaluation code are publicly available at: https://github.com/siri-rouser/RoundaboutHD.git

Related papers

• A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic [2.024925013349319]
  The proposed framework is evaluated on the 5th AI City Challenge dataset (Track 3), comprising 46 camera feeds.<n>The framework achieves competitive performance with an IDF1 score of 0.8289, and precision and recall scores of 0.9026 and 0.8527 respectively.
  arXiv  Detail & Related papers  (2025-05-01T14:00:25Z)
• An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras [0.13092499936969584]
  Real time vehicle detection is a challenging task for urban traffic surveillance.<n>Fish eye cameras are widely used in real time vehicle detection purpose to provide large area coverage and 360 degree view at junctions.<n>To overcome challenges such as light glare from vehicles and street lights, shadow, non-linear distortion, scaling issues of vehicles and proper localization of small vehicles, a modified YOLOv5 object detection scheme is proposed.
  arXiv  Detail & Related papers  (2025-02-06T23:42:05Z)
• RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments [62.5830455357187]
  We setup an egocentric multi-sensor data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye)<n>A large-scale multimodal dataset is constructed, named RoboSense, to facilitate egocentric robot perception.
  arXiv  Detail & Related papers  (2024-08-28T03:17:40Z)
• MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark [63.878793340338035]
  Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. Existing datasets for this task are either synthetically generated or artificially constructed within a controlled camera network setting. We present MTMMC, a real-world, large-scale dataset that includes long video sequences captured by 16 multi-modal cameras in two different environments.
  arXiv  Detail & Related papers  (2024-03-29T15:08:37Z)
• Vehicle Perception from Satellite [54.07157185000604]
  The dataset is constructed based on 12 satellite videos and 14 synthetic videos recorded from GTA-V. It supports several tasks, including tiny object detection, counting and density estimation. 128,801 vehicles are annotated totally, and the number of vehicles in each image varies from 0 to 101.
  arXiv  Detail & Related papers  (2024-02-01T15:59:16Z)
• The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking [4.799822253865053]
  This work presents a novel video dataset recorded from overlapping highway traffic cameras along an urban interstate, enabling multi-camera 3D object tracking in a traffic monitoring context. Data is released from 3 scenes containing video from at least 16 cameras each, totaling 57 minutes in length. 877,000 3D bounding boxes and corresponding object tracklets are fully and accurately annotated for each camera field of view and are combined into a spatially and temporally continuous set of vehicle trajectories for each scene.
  arXiv  Detail & Related papers  (2023-08-28T18:43:33Z)
• DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes [74.64897845999677]
  We introduce a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT.
  arXiv  Detail & Related papers  (2023-02-15T14:10:42Z)
• Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities [4.4855664250147465]
  We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes.
  arXiv  Detail & Related papers  (2022-08-30T11:36:07Z)
• Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
  We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
  arXiv  Detail & Related papers  (2022-04-15T12:47:01Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• TJU-DHD: A Diverse High-Resolution Dataset for Object Detection [48.94731638729273]
  Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods. We build a diverse high-resolution dataset (called TJU-DHD) The dataset contains 115,354 high-resolution images and 709,330 labeled objects with a large variance in scale and appearance.
  arXiv  Detail & Related papers  (2020-11-18T09:32:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.