Abstract：For multiple target tracking algorithm in the traditional pedestrian detection speed is slow, vulnerable to illumination change, the fast moving of pedestrians and the influence of partial occlusion cause the poor performance of pedestrian target tracking. According to the classic Tracking-by-Detection mode, a new pedestrian tracking algorithm is proposed, which uses deep learning YOLOv3 algorithm to detect pedestrian targets, and then uses fast corner detection algorithm and brisk feature point description algorithm to match the feature points of pedestrian targets between adjacent frames to achieve multi-target pedestrian tracking.The experimental results show that the pedestrian target achieves good continuous tracking effect under various complex environments of backlight, fast movement and partial occlusion, with an average accuracy of 87.7% and a speed of 35 frames per second.
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2021, Vol. 42 
