.png){kind=icon}
返回
A real time target face tracking algorithm based on saliency detection and Camshift
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-14889-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To take advantage of the speed advantage of the Camshift and try to overcome the problem of its poor robustness in target tracking, in this paper, a real time target face tracking algorithm based on saliency detection and Camshift is proposed. Considering that the target to be tracked is more significant than the background in the frame, the saliency detection algorithm MBplus is first used to remove the background around the target as much as possible, so as to reduce the interference caused by the background to the Camshift tracking results. Then the Camshift is used to search and localize the targets in the processed video frames. At the same time, to compensate for the lack of tracking ability of Camshift for some characteristic targets, the Kalman filter is used to predict the position of the target in the current frame. Finally, the Kalman-predicted target position, the target position obtained by Camshift, are compared with the target tracked in the previous frame, and the position with high similarity is considered as the target tracking result of this paper. The experimental results show that the average tracking precision of the proposed target face tracking algorithm on the Birchfield database is 94.0%, its average tracking success rate on the NRC-IIT Facial Video Database is 100%, and even for the target faces with few attributes in ytcelebrity database, its tracking precision and tracking success rate are all 100%, which are superior to some state-of-the-art tracking algorithms.
Keyword:
Saliency detection
Kalman filter
Camshift algorithm
Face tracking
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
.png){kind=icon}
Improved Camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman基于AKAZE和Kalman改进的Camshift目标跟踪算法
OAAI
OAAI分享收藏分享收藏
查看更多内容