Based on YOLO v3 Target Recognition Algorithm as Vehicle Tracking Algorithm Analysis

Abstract

Traditional traffic information acquisition and acquisition are mainly implemented by sensors, and these traditional acquisition and acquisition systems have some great drawbacks. However, with the popularity of traffic monitoring, computer vision technology gradually has a platform foundation that can be applied to identify and track traffic conditions. In this paper, through the research of traditional and Deep learning-based multi-target recognition algorithms and two common multi-target tracking algorithms, a solution of YOLO v3 network combined with deep-sort algorithm is proposed. In this paper, a video of traffic information of urban roads is directly collected for areas with relatively large traffic flow. Interval frames are extracted from the video data set to make relevant data sets for training and verification of YOLO v3 neural networks. Combined with the test results, an open source vehicle depth model dataset is used to train the vehicle depth feature weight file, and Deep-SORT algorithm is used to achieve the target tracking, which can realize the real-time and more accurate multi-target recognition and tracking of moving vehicles.