Pedestrian Tracking Algorithm Based on Deep Learning and Color Features
Aiming at the problems of low pedestrian tracking accuracy and slow tracking speed caused by pedestrian occlusion in the pedestrian tracking algorithm,this paper proposes a pedestrian tracking algorithm based on deep learning and color features.First,it uses the yolov5 target detection algorithm to detect pedestrians,and obtains video frames with pedestrian frames.At the same time,the coordinate information of the detection frame is used to determine whether there is occlusion between pedestrians.If there is occlusion,the pixels of the occlusion area is set to 0,and the non-occlusion area is segmented,the non-occluded area is converted into the HSV color space,the HSV component is quantized,a color feature histogram is constructed,and it is expressed as a one-dimensional vector G.Secondly,the pedestrian tracking model is constructed based on the coordinates of the pedestrian de-tection frame in the first frame,the tracking object is initialized,and the pedestrian position is predicted according to the change of the pedestrian's centroid.Tested on the public data set MOT-16 data set,the MOTA is 49.78%,which is 1.51%and 0.33%higher than the Sort and DeepSort algorithms,respectively,and 7.07%and 3.46%higher than the Sort and DeepSort algorithms in the IDF1 score.The tracking speed is 24%higher than that of DeepSort.
deep learningobject detectionobject trackingHSV color featuresMOT-16 dataset
国家科技期刊平台
NSTL
万方数据
