Multiple Hypothesis Detection and Tracking Using Deep Learning for Video Traffic Surveillance

• Published in: IEEE access Vol. 9; pp. 164282 - 164291
• Main Authors: Abdelali, Hamd Ait, Derrouz, Hatim, Zennayi, Yahya, Thami, Rachid Oulad Haj, Bourzeix, Francois
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer vision; Convolutional neural networks; data association; Deep learning; detection; Hypotheses; Intelligent transportation systems; Kalman filter; Kalman filters; Machine learning; multiple hypotheses tracking; Occlusion; occlusion handling; Predictive models; Surveillance; Target tracking; Tracking; Traffic management; Traffic surveillance; Trajectory; Vehicles; Video sequences
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3133529

Moroccan Intelligent Transport System is the first Moroccan system that uses the latest advances in computer vision, machine learning and deep learning techniques to manage Moroccan traffic and road violations. In this paper, we propose a fully automatic approach to Multiple Hypothesis Detection and Tracking (MHDT) for video traffic surveillance. The proposed framework combines Kalman filter and data association-based tracking methods using YOLO detection approach, to robustly track vehicles in complex traffic surveillance scenes. Experimental results demonstrate that the proposed approach is robust to detect and track the trajectory of the vehicles in different situations such as scale variation, stopped vehicles, rotation, varying illumination and occlusion. The proposed approach shows a competitive results (detection: 94.10% accuracy, tracking: 92.50% accuracy) compared to the state-of-the-art approaches.