Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery

• URL: http://arxiv.org/abs/2411.02136v1
• Date: Mon, 04 Nov 2024 14:49:01 GMT
• Title: Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
• Authors: Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis,
• Abstract summary: This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage. We employ state-of-the-art computer vision and deep learning to create an end-to-end pipeline. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise, cost-effective urban traffic monitoring.
• Score: 4.387337528923525
• License:
• Abstract: This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage, addressing key challenges in urban traffic monitoring and limitations of traditional ground-based systems. We employ state-of-the-art computer vision and deep learning to create an end-to-end pipeline that enhances vehicle detection, tracking, and trajectory stabilization. Conducted in the Songdo International Business District, South Korea, the study used a multi-drone experiment over 20 intersections, capturing approximately 12TB of 4K video data over four days. We developed a novel track stabilization method that uses detected vehicle bounding boxes as exclusion masks during image registration, which, combined with advanced georeferencing techniques, accurately transforms vehicle coordinates into real-world geographical data. Additionally, our framework includes robust vehicle dimension estimation and detailed road segmentation for in-depth traffic analysis. The framework produced two high-quality datasets: the Songdo Traffic dataset, comprising nearly 1 million unique vehicle trajectories, and the Songdo Vision dataset, containing over 5,000 human-annotated frames with about 300,000 vehicle instances in four classes. Comparisons between drone-derived data and high-precision sensor data from an instrumented probe vehicle highlight the accuracy and consistency of our framework's extraction in dense urban settings. By publicly releasing these datasets and the pipeline source code, this work sets new benchmarks for data quality, reproducibility, and scalability in traffic research. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise, cost-effective urban traffic monitoring, providing valuable resources for the research community to develop intelligent transportation systems and improve traffic management strategies.

Related papers

• RoboSense: Large-scale Dataset and Benchmark for Multi-sensor Low-speed Autonomous Driving [62.5830455357187]
  In this paper, we construct a multimodal data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye) A large-scale multi-sensor dataset is built, named RoboSense, to facilitate near-field scene understanding. RoboSense contains more than 133K synchronized data with 1.4M 3D bounding box and IDs in the full $360circ$ view, forming 216K trajectories across 7.6K temporal sequences.
  arXiv  Detail & Related papers  (2024-08-28T03:17:40Z)
• 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)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras [26.457695296042903]
  We propose SKoPe3D, a unique synthetic vehicle keypoint dataset from a roadside perspective. SKoPe3D contains over 150k vehicle instances and 4.9 million keypoints. Our experiments highlight the dataset's applicability and the potential for knowledge transfer between synthetic and real-world data.
  arXiv  Detail & Related papers  (2023-09-04T02:57:30Z)
• 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)
• The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset [4.413278371057897]
  Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. We use an intelligent public inner-city intersection in Germany with visual sensor technology. The resulting dataset consists of eight hours of measurement data.
  arXiv  Detail & Related papers  (2023-07-12T13:46:20Z)
• TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems [2.748428882236308]
  We develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images.
  arXiv  Detail & Related papers  (2023-03-01T09:03:44Z)
• CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins [1.981804802324697]
  CitySim has vehicle trajectories extracted from 1140 minutes of drone videos recorded at 12 locations. CitySim was generated through a five-step procedure that ensured trajectory accuracy.
  arXiv  Detail & Related papers  (2022-08-23T15:24:53Z)
• 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)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)

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.