计算机与数字工程2024，Vol.52Issue(1)：251-258,8.DOI:10.3969/j.issn.1672-9722.2024.01.042
基于深度学习和颜色特征的行人跟踪算法
Pedestrian Tracking Algorithm Based on Deep Learning and Color Features
摘要
Abstract
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.关键词
深度学习/目标检测/目标跟踪/HSV颜色特征/MOT-16数据集Key words
deep learning/object detection/object tracking/HSV color features/MOT-16 dataset分类
信息技术与安全科学引用本文复制引用
曹建荣,李凯,尚硕,韩发通,庄园,朱亚琴..基于深度学习和颜色特征的行人跟踪算法[J].计算机与数字工程,2024,52(1):251-258,8.基金项目
国家自然科学基金项目(编号:62073196,U1806204) （编号:62073196,U1806204）
山东省重点研发计划(编号:2019GSF111054)资助. （编号:2019GSF111054）
