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Title	Leveraging Deep Learning For Improving Real Time Stolen Vehicle Tracking In Smart Cities
ID_Doc	35059
Authors	Sivasubramanian K.; Balasundaram Sathiya Devi V.; Bala Murugan M.S.; Rajagopal M.K.
Year	2023
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3638985.3639026
Abstract	Efficiently detecting and tracking stolen vehicles is a crucial aspect of law enforcement and public safety. Traditional methods, reliant on limited data sources, are time-consuming and labor-intensive due to manual efforts. The emergence of deep learning technology offers the potential to streamline these processes, reducing manual work and saving valuable time. This study proposes a deep learning-based system for the surveillance of lost or stolen vehicles, utilizing the YOLOv7 object detection model and the DeepSORT tracking algorithm. The proposed system automates the process of data generation, detection, and tracking across multiple camera feeds, resulting in an interactive map that displays the vehicle trajectory. Using a publicly available dataset containing a set of camera recordings of vehicles, the dataset for the experiment is generated and enhanced by augmentation techniques. The experimental dataset encompasses a video segment highlighting the target vehicle of interest. This footage is processed in detail to yield image-label pairs, encapsulating the target vehicle and its distinctive features, thereby serving as the training foundation. Further, the resultant model is tested on varied video sets within the public dataset and the detection results are recorded for performance analysis. The proposed system's performance, evaluated through various metrics, effectively detects and tracks stolen vehicles in real-Time scenarios. This research enhances public safety by empowering law enforcement agencies with a powerful tool for rapid identification and recovery of stolen vehicles. © 2023 ACM.
Author Keywords	augmentation; DeepSORT; Surveillance; target vehicle; YOLOv7

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