Camshift tracking method based on correlation probability graph for model pig

Abstract

Abstract 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 the effectiveness of the non-significant model pig tracking in breeding environment, a Camshift tracking approach based on correlation probability graph, i.e., C a m T r a c o r −P G , is proposed in this paper, in which the correlation probability graph is introduced to achieve target positioning and tracking. Technically, acquiring images through a vision sensor, according to the circular arrangement of pixels in the inverse probability projection graph, and multiplying the inverse projection probability value of a pixel by its surrounding pixels 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.

Keywords

• Identification and tracking
• Model pigs
• Vision sensor
• Correlation probability