Affiliation:
1. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, Málaga, Spain
2. Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, C/Severo Ochoa, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, Spain
Abstract
Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic videos.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications,Theoretical Computer Science,Software
Reference63 articles.
1. Machine learning for video event recognition;Avola;Integrated Computer-Aided Engineering.,2021
2. Object detection using depth completion and camera-LiDAR fusion for autonomous driving;Carranza-García;Integrated Computer-Aided Engineering.,2022
3. Liu W, Liao S, Ren W, Hu W, Yu Y. High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019. pp. 5182-5191.
4. Deep convolutional neural networks for pedestrian detection;Tomè;Signal Processing: Image Communication.,2016
5. Automated image capturing using CNN and RNN;Sehgal;International Journal of Research in Engineering, Science and Management.,2022
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