Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos
Open accessAbstract
Along with the advancement of light-weight sensing and processing technologies, unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent traffic monitoring and control. UAV-mounted cameras can capture traffic-flow videos from various perspectives providing a comprehensive insight into road conditions. To analyze the traffic flow from remotely captured videos, a reliable and accurate vehicle detection-and-tracking approach is required. In this paper, we propose a deep-learning framework for vehicle detection and tracking from UAV videos for monitoring traffic flow in complex road structures. This approach is designed to be invariant to significant orientation and scale variations in the videos. The detection procedure is performed by fine-tuning a state-of-the-art object detector, You Only Look Once (YOLOv3), using several custom-labeled traffic datasets. Vehicle tracking is conducted following a tracking-by-detection paradigm, where deep appearance features are used for vehicle re-identification, and Kalman filtering is used for motion estimation. The proposed methodology is tested on a variety of real videos collected by UAVs under various conditions, e.g., in late afternoons with long vehicle shadows, in dawn with vehicles lights being on, over roundabouts and interchange roads where vehicle directions change considerably, and from various viewpoints where vehicles’ appearance undergo substantial perspective distortions. The proposed tracking-by-detection approach performs efficiently at 11 frames per second on color videos of 2720p resolution. Experiments demonstrated that high detection accuracy could be achieved with an average F1-score of 92.1%. Besides, the tracking technique performs accurately, with an average multiple-object tracking accuracy (MOTA) of 81.3%. The proposed approach also addressed the shortcomings of the state-of-the-art in multi-object tracking regarding frequent identity switching, resulting in a total of only one identity switch over every 305 tracked vehicles.
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The Faster R-CNN with ResNet-50 achieves an F1 score of 99.10% on a custom dataset.
“[93] Faster R-CNN | ResNet-50 | Custom | 4096 × 2160 | 50m | Static | One class (Vehicle) | -- | 99.10 | 98.30 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [93]
The Faster R-CNN with ResNet-50 achieves an accuracy of 98.30% on a custom dataset.
“[93] Faster R-CNN | ResNet-50 | Custom | 4096 × 2160 | 50m | Static | One class (Vehicle) | -- | 99.10 | 98.30 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [93]
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A Novel Keypoint Supplemented R-CNN for UAV Object Detection
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Small-Object Detection for UAV-Based Images
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Real-Time Unmanned Aerial Vehicle-Based Traffic State Estimation for Multi-Regional Traffic Networks
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Deep Learning-based Trajectory Estimation of Vehicles in Crowded and Crossroad Scenarios
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DB-Tracker: Multi-Object Tracking for Drone Aerial Video Based on Box-MeMBer and MB-OSNet
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Design and Application Research of a UAV-Based Road Illuminance Measurement System
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Improved UAV-to-Ground Multi-Target Tracking Algorithm Based on StrongSORT
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EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
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Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx
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A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection
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Self-citationEnhanced Nighttime Vehicle Detection for On-Board Processing
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YOLOV10 vs. YOLOV8: Performance Improvements for Vehicle Detection at Multilane Roundabouts
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A machine learning pipeline for extracting decision-support features from traffic scenes1
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Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images
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Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Autonomous Aerial Vehicle Object Detection
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CAMOT: Content Aware Multi Object Tracking
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A hybrid vehicle tracking System for Low-power Embedded Devices
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Robust vehicle detection under different weather conditions
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GEOTrat Points as a free resource in QGIS for mapping the performance of agricultural experiments
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Real-Time Object Detection in Video Surveillance Systems Using Machine Learning
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A fast method for detecting rock blocks deposits and calculating volumes and 3D surface areas
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- Authors
- Wang, J., Simeonova, S., & Shahbazi, M.
- Affiliations
- University of Calgary (CA)
- Corresponding author
- Shahbazi, Mozhdeh
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2019-09-16
- Volume
- 11
- Issue
- 18
- Pages
- 2155
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2973851319
- Cited by
- 50
- References
- 80
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 14 · 2023: 10 · 2022: 8 · 2021: 7 · 2020: 2
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada (Discovery)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vehicle detection, vehicle tracking, unmanned aerial vehicles, UAV video, traffic flow monitoring, orientation invariance, scale invariance, tracking-by-detection, vehicle re-identification, Kalman filter, multi-target tracking, identity switching, perspective distortion
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