Abstract. We propose a new algorithm for automatic detect violations of traffic rules for improving the people safety on the unregulated pedestrian crossing. The algorithm uses multi-step proceedings. They are zebra detection, cars detection, and pedestrian detection. For car detection, we use faster R-CNN deep learning tool. The algorithm shows promising results in the detection violations of traffic rules.

This article describes the relevance of developing methods and systems for detection photo-video violations of the Rules of the road. The proposed method includes several steps: 1) detecting of the three classes of objects on a video sequence (pedestrian crossing, a motor vehicle and a human on the pedestrian crossing; 2) tracking the trajectories of the vehicle and the human on the pedestrian crossing; 3) comparing the paths of the pedestrian and the vehicle and determining whether there has been a violation of the Rules of the road for a certain period of time. For real-time object detection, we used neural network YOLO V3.

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The article presents a method for recovering lost sections of the map of the underlying surface. We consider spatial autocorrelation as image pre-processing for its subsequent analysis when restoring a part of the map in the direction of the radar carrier course. An algorithm for recovering images of lost areas has been proposed and a software implementing the algorithm has been developed. The efficiency of the developed algorithm has been evaluated on a test set of images using a statistical criterion.