PEDESTRIAN DETECTION AND TRACKING OF THEIR MOVEMENT TRAJECTORY USING THE BACKGROUND SEGMENTATION METHOD BASED ON KNN

Abstract

Problem statement. Target detection and video image tracking is one of the important topics of computer vision, as well as a problem that needs to be urgently addressed in practical applications. Interference makes it difficult to get the target position. Faced with this problem, scientists have proposed many tracking algorithms. Purpose. In real video monitoring, the system can automatically detect the foreground and draw the trajectory of the foreground. Methods. Use the KNN background segmenting algorithm in combination with OpenCV to detect the foreground and track the trajectory of the video. Novelty. It can continuously detect the foreground in the video and is also applicable to the new foreground in the video. This method is easy to call, does not require the use of a large amount of computer performance resources and can achieve real-time detection and tracking. Result. In a real test, we got good test results, we successfully identified moving pedestrians on video and drew their trajectories. Practical relevance. The algorithm can be applied to road traffic, can determine the trajectory of a vehicle to track vehicles, and can also be used to detect pedestrians to pave the way for subsequent recognition of pedestrian behavior.