TY - GEN
T1 - Object tracking algorithm based on combination of dynamic template matching and kalman filter
AU - Zheng, Bin
AU - Xu, Xiangyang
AU - Dai, Yaping
AU - Lu, Yuanyuan
PY - 2012
Y1 - 2012
N2 - The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance.
AB - The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance.
KW - Dynamic template update
KW - Extraction of feature points
KW - Inter-frame difference
KW - Kalman filter
KW - Sift
UR - https://www.scopus.com/pages/publications/84868111254
U2 - 10.1109/IHMSC.2012.129
DO - 10.1109/IHMSC.2012.129
M3 - Conference contribution
AN - SCOPUS:84868111254
SN - 9780769547213
T3 - Proceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
SP - 136
EP - 139
BT - Proceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
T2 - 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
Y2 - 26 August 2012 through 27 August 2012
ER -