Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos

Article type: Research Article

Authors: Paul, Ann Rija^{; *} | Grace Mary Kanaga, E.

Affiliations: CSE Department, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

Correspondence: [*] Corresponding author. Ann Rija Paul. E-mail: [email protected].

Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.

Keywords: Vehicle tracking, surveillance, collision detection, trajectory and angle of intersection, deep convolutional neural network

DOI: 10.3233/JIFS-235911

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4803-4816, 2024