TY - GEN
T1 - An improved mean shift algorithm for moving object tracking
AU - Li, Ning
AU - Zhang, Dan
AU - Gu, Xiaorong
AU - Huang, Li
AU - Liu, Wei
AU - Xu, Tao
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/19
Y1 - 2015/6/19
N2 - Moving object tracking is one of the key technologies in video surveillance. Mean shift algorithm fails to track the moving object in complicated environment. In this paper, a new strategy is proposed to improve the tracking ability of mean shift algorithm, in which the contrast between object and background along with similarity evaluation are applied for generating and updating object model. To eliminate the interference of the most similar features between tracking object and background, the coefficient ratio of the object to surrounding environment is first imported to generate the object model. To make sure the accuracy of updating object model, the effective way that combines similarity evaluation and Kalman filtering prediction is then applied for judge whether the tracking object is sheltered by other objects or background. The experimental results have shown that the proposed method can tack the moving object stably.
AB - Moving object tracking is one of the key technologies in video surveillance. Mean shift algorithm fails to track the moving object in complicated environment. In this paper, a new strategy is proposed to improve the tracking ability of mean shift algorithm, in which the contrast between object and background along with similarity evaluation are applied for generating and updating object model. To eliminate the interference of the most similar features between tracking object and background, the coefficient ratio of the object to surrounding environment is first imported to generate the object model. To make sure the accuracy of updating object model, the effective way that combines similarity evaluation and Kalman filtering prediction is then applied for judge whether the tracking object is sheltered by other objects or background. The experimental results have shown that the proposed method can tack the moving object stably.
KW - Kalman filtering prediction
KW - mean shift algorithm
KW - moving object tracking
KW - object model generation
KW - video surveillance
UR - http://www.scopus.com/inward/record.url?scp=84938328320&partnerID=8YFLogxK
U2 - 10.1109/CCECE.2015.7129489
DO - 10.1109/CCECE.2015.7129489
M3 - Conference article published in proceeding or book
AN - SCOPUS:84938328320
T3 - Canadian Conference on Electrical and Computer Engineering
SP - 1425
EP - 1429
BT - 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering, CCECE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 28th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2015
Y2 - 3 May 2015 through 6 May 2015
ER -