Research on Vision-Based Traffic Scene Object Detection and Tracking Technology
A target detection and tracking algorithm suitable for traffic scenes is proposed to address the issues of small target missed detection and false detection in detecting multiple targets using general object detection algorithms.First,an anchor box suitable for object detection in traffic scenes was calculated based on the K-means++ clustering algorithm,and depthwise separable convolution was used to optimize the object detection algorithm.Second-ly,the Hungarian matching algorithm was used to realize the correlation matching between the predicted valueofthe Kalman filter algorithm and the actual value,so as to realize the target tracking.The experimental results show that compared with the original algorithm,the mAP of the improved object detection and tracking algorithm in this paper is improved by 2.3%to 99.13%,and speed is increased by 20%to 30 frames per second.The targets in the traffic scene are accurately detectedandtracked,maintaining the great real-time performance.
Object detectionTarget trackingKalman filterHungarian match
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