Moving vehicle detection based on RAFT optical flow and YOLOV4 in dynamic background

Meng Yu; Hongfei Yu; Xiaoguang Pan; Yang Cao; Jin Lv; Fei Pang

doi:10.1117/12.2684930

19 October 2023 Moving vehicle detection based on RAFT optical flow and YOLOV4 in dynamic background

Meng Yu, Hongfei Yu, Xiaoguang Pan, Yang Cao, Jin Lv, Fei Pang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127095S (2023) https://doi.org/10.1117/12.2684930
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

A new moving target detection algorithm is proposed to solve the problems of poor target detection integrity and interference by outliers in current moving target detection methods based on dynamic background. The algorithm combines the RAFT optical flow method with the YOLOV4 detection method. First, the offset between the matching pixels is obtained through the RAFT optical flow method. The epipolar constraint relationship between the matching pixels is solved based on the offset. The constraint relationship is used to solve the candidate frame of the foreground target edge, Then the method of calculating the coincidence degree between the candidate frame of the foreground target edge and the candidate frame of the moving target detected by the YOLOV4 network is used to preliminarily extract the moving target area, which improves the accuracy of the moving target detection. Finally, the final moving target region is obtained after post-processing the initially extracted internal area of the moving target. The experimental results show that compared with the algorithm without the YOLOV4 network, the precision accuracy and F-measure of the algorithm on kitti's public dataset are improved, and more accurate segmentation results can be obtained.

Citation Download Citation

Meng Yu, Hongfei Yu, Xiaoguang Pan, Yang Cao, Jin Lv, and Fei Pang "Moving vehicle detection based on RAFT optical flow and YOLOV4 in dynamic background", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127095S (19 October 2023); https://doi.org/10.1117/12.2684930

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KEYWORDS

Target detection

Optical flow

Image segmentation

Detection and tracking algorithms

Matrices

Feature extraction

Cameras

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