Camshift Tracking Method Based on Correlation Probability Graph for Model Pig
Xiangnan Zhang, Yongtao Liu, Wenwen Gong, Qifeng He, Haolong Xiang, Dan Li, Yawei Wang, Yifei Chen, Yaqian Deng, Fanglin Geng · 2019
The identification and tracking for model pigs, as a vital research content for studying the habits of model pigs, drawed more and more considerable attention. To fulfill people requirements for effectiveness of the non-significant model pig tracking in breeding environment, a Camshift tracking approach based on correlation probability graph, i.e., CamTracor-PG, is proposed in this paper, in which the correlation probability graph is introduced to achieve target positioning and tracking. Technically, according to the circular arrangement of pixels in the inverse probability projection graph, multiplying the inverse projection probability value of a pixel by its surrounding pixels, which could obtain the weighted sum. Then, the target projection grayscale graph is established by utilizing the correlation probability value for positioning, identification and tracking of model pigs. Finally, extensive experiments are conducted to validate reliability and efficiency of our approach.