Edge-Computing-Empowered Vehicle Tracking and Speed Estimation Against Strong Image Vibrations Using Surveillance Monocular Camera

Lashkov, Igor; Yuan, Runze; Zhang, Guohui

doi:10.1109/tits.2023.3318077

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Cited by 3 publications

References 26 publications

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Real-time monitor heading dates of wheat accessions for breeding in-field based on DDEW-YOLOv7 model and BotSort algorithm

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Zhang,

Pan

et al. 2025

Expert Systems with Applications

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Real-time monitor heading dates of wheat accessions for breeding in-field based on DDEW-YOLOv7 model and BotSort algorithm

Song,

Zhang,

Pan

et al. 2025

Expert Systems with Applications

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AI-Powered Video Monitoring: Assessing the NVIDIA Jetson Orin Devices for Edge Computing Applications

Scalcon,

Tahal,

Ahrabi

et al. 2024

2024 IEEE Transportation Electrification Conference and Expo (ITEC)

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Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles

Murendeni,

Mwanza,

Obagbuwa

2024

WEVJ

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This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency.

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Edge-Computing-Empowered Vehicle Tracking and Speed Estimation Against Strong Image Vibrations Using Surveillance Monocular Camera

Cited by 3 publications

References 26 publications

Real-time monitor heading dates of wheat accessions for breeding in-field based on DDEW-YOLOv7 model and BotSort algorithm

Real-time monitor heading dates of wheat accessions for breeding in-field based on DDEW-YOLOv7 model and BotSort algorithm

AI-Powered Video Monitoring: Assessing the NVIDIA Jetson Orin Devices for Edge Computing Applications

Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles

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