A Video Surveillance-based Enhanced Collision Prevention and Safety System
collision warning system; enhanced vision-based system; non max suppression; pedestrian detection; vehicle detection; video surveillance; YOLOv3
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023.
14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023, Vol-2023-June, pp. 2346-2354.
Loungani B., Department of Data Science, Christ University, Pune, India; Agrawal J., Department of Data Science, Christ University, Pune, India; Jacob L., Department of Data Science, Christ University, Pune, India