Real-Time Airborne Target Tracking using DeepSort Algorithm and Yolov7 Model

• Published in: International journal of advanced computer science & applications Vol. 15; no. 2
• Main Authors: Ghazlane, Yasmine, Alaoui, Ahmed El Hilali, Medomi, Hicham, Bnouachir, Hajar
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
• Subjects: Algorithms; Artificial intelligence; Computer vision; Cybersecurity; Drone aircraft; Drones; Security systems; Target detection; Target recognition; Tracking
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150248

In light of the explosive growth of drones, it is more critical than ever to strengthen and secure aerial security and privacy. Drones are used maliciously by exploiting some gaps in artificial intelligence and cybersecurity. Airborne target detection and tracking tasks have gained paramount importance in various domains, encompassing surveillance, security, and traffic management. As airspace security systems aiming to regulate drone activities, anti-drones leverage mostly artificial intelligence and computer vision advances in the used detection and tracking models to perform effectively and accurately airborne target detection, identification, and tracking. The reliability of the anti-drone systems relies mostly on the ability of the incorporated models to satisfy an optimal compromise between speed and performance in terms of inference speed and used detection evaluation metrics since the system should recognize the targets effectively and rapidly to take appropriate actions regarding the target. This research article explores the efficacy of DeepSort algorithm coupled with YOLOv7 model in detecting and tracking five distinct airborne targets namely, drones, birds, airplanes, daytime frames, and buildings across diverse contexts. The used DeepSort and Yolov7 models aim to be used in anti-drone systems to detect and track the most encountered airborne targets to reinforce airspace safety and security. The study conducts a comparative analysis of tracking performance under different scenarios to evaluate the algorithm's versatility, robustness, and accuracy. The experimental results show the effectiveness of the proposed approach.