Vehicle tracking method and system based on FCOS

Abstract

The invention provides a vehicle tracking method and system based on FCOS, and belongs to the technical field of vehicle tracking. According to the method, a vehicle in a video is detected through anFCOS model; deep learning features and edge features are fused to serve as feature description of the vehicle; by comparing vehicle features, vehicles with the highest similarity of adjacent frame features are matched, small tracks corresponding to the vehicles are generated in a fixed time window, the similarity between the small tracks is measured through a convolutional neural network, and thetracks with the highest similarity are connected, a complete track is obtained, and the whole tracking process is completed. According to the method, the accuracy of vehicle detection can be effectively improved, the influence caused by shielding, camera movement and other factors is reduced, and the vehicle tracking accuracy is improved.

Description

technical field [0001] The invention belongs to the technical fields of artificial intelligence and computer vision, and in particular relates to a vehicle tracking method based on FCOS. Background technique [0002] With the rapid development of today's economy, as the main means of transportation and transportation, the number of vehicles has increased rapidly, resulting in the continuous deterioration of traffic conditions and frequent occurrence of traffic accidents. Now no matter which country is troubled by traffic problems in varying degrees without exception. In order to realize the intelligent management of road traffic, many research institutions all over the world are sparing no effort in the research and development of various intelligent products. However, due to the lack of accuracy of vehicle detectors, camera movement, occlusion and other problems, vehicle tracking cannot achieve high accuracy. Since the current vehicle tracking mainly relies on the vehicle...

AI Technical Summary

Problems solved by technology

However, due to the lack of accuracy of vehicle detectors, camera movement, occlusion and other problems, vehicle tracking cannot achieve high accuracy.

Since the current vehicle tracking mainly relies on the vehicle detection based on the box regression model YOLO, and the FCOS network based on the pixel-by-pixel regression is more complex, which brings difficulties to apply FCOS to vehicle tracking.

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