TY - GEN
T1 - Target tracking based on multi-color joint probability statistics model
AU - Li, Hongguang
AU - Ding, Wenrui
AU - Liu, Chunhui
AU - Zheng, Junling
PY - 2011
Y1 - 2011
N2 - For real-time target tracking under complex scene, a target tracking algorithm based on multi-color joint probability analysis model was presented. The algorithm adopted color histogram to represent the target statistical characteristic with Camshift principle, and carried out exploratory research in such aspects as multichannel joint color features statistics, projection map area weighted processing, the tracking window size and position calculating, algorithm processing mechanism of circulation. It used red (R), green (G), blue (B), hue(H), luminance (Y) channel color as the target observed characteristics, and designed the calculation method based on the probability statistic to distinguish any color target from complex scene. It also established the calculation method for tracking window size and position which adapted the multi-color model. Using weighting projection map area method, the background interference around the target potential area was eliminated. At last, more reasonable convergence judgment and the algorithm cycle rules were put forward. After the experimental certification, the real-time performance and detection ratio present a good result.
AB - For real-time target tracking under complex scene, a target tracking algorithm based on multi-color joint probability analysis model was presented. The algorithm adopted color histogram to represent the target statistical characteristic with Camshift principle, and carried out exploratory research in such aspects as multichannel joint color features statistics, projection map area weighted processing, the tracking window size and position calculating, algorithm processing mechanism of circulation. It used red (R), green (G), blue (B), hue(H), luminance (Y) channel color as the target observed characteristics, and designed the calculation method based on the probability statistic to distinguish any color target from complex scene. It also established the calculation method for tracking window size and position which adapted the multi-color model. Using weighting projection map area method, the background interference around the target potential area was eliminated. At last, more reasonable convergence judgment and the algorithm cycle rules were put forward. After the experimental certification, the real-time performance and detection ratio present a good result.
KW - Camshif
KW - multi-color model
KW - probability statistics
KW - target tracking
UR - https://www.scopus.com/pages/publications/84862924944
U2 - 10.1109/CISP.2011.6100008
DO - 10.1109/CISP.2011.6100008
M3 - 会议稿件
AN - SCOPUS:84862924944
SN - 9781424493067
T3 - Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011
SP - 436
EP - 440
BT - Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011
T2 - 4th International Congress on Image and Signal Processing, CISP 2011
Y2 - 15 October 2011 through 17 October 2011
ER -