Open AccessAn Algorithm for Target Tracking of the Car in Accident
Author(s) -
Siyuan Zhang,
Yunqing Liu,
Yile Dai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012026
Subject(s) - bhattacharyya distance , tracking (education) , computer science , computer vision , artificial intelligence , feature (linguistics) , kalman filter , vehicle tracking system , matching (statistics) , position (finance) , interference (communication) , rotation (mathematics) , algorithm , feature extraction , mathematics , psychology , computer network , pedagogy , linguistics , philosophy , statistics , channel (broadcasting) , finance , economics
At present, the target tracking algorithm for the video of the vehicle involved in the accident is not perfect, which causes the target to rotate and the target is blocked, and the tracking effect is not good. This paper uses a CamShift tracking algorithm that combines HLBP feature matching and unscented Kalman filtering. Firstly, the vehicle features are extracted through the algorithm to obtain more accurate features, thereby reducing the interference caused by the target rotation on the feature extraction. Secondly, the degree of occlusion of the target is judged by the Bhattacharyya distance, and finally the UKF algorithm is used to predict the target position. The vehicle efficiently solve the problem of poor tracking performance when the target is occluded. Experiments have proved that, in the actual application of tracking the accident vehicle, the algorithm can effectively reduce the influence of external factors on the tracking effect, and the tracking accuracy is greatly improved.