Novel method for real-time detection and tracking of pig body and its different parts - Chen, Fuen; Liang, Xiaoming; Chen, Longhan; Liu, Baoyuan; Lan, Yubin (International Journal of Agricultural and Biological Engineering, 2020)
Detection and tracking of all major parts of pig body could be more productive to help to analyze pig behavior. To achieve this goal, a real-time algorithm based on You Only Look At CoefficienTs (YOLACT) was proposed. A pig body was divided into ten parts: one head, one trunk, four thighs and four shanks. And the key points of each part were calculated by the novel algorithm, which was based mainly on combination of the Zhang-Suen thinning algorithm and Gravity algorithm. The experiment results showed that these parts of pig body could be detected and tracked, and their contributions to overall pig activity could also be sought out. The detect accuracy of the algorithm in the data set could reach up to 90%, and the processing speed to 30.5 fps. Furthermore, the algorithm was robust and adaptive. Keywords: computer vision, CNN, pig, YOLACT, detection and tracking DOI: 10.25165/j.ijabe.20201306.5820 Citation: Chen F E, Liang X M, Chen L H, Liu B Y, Lan Y B. Novel method for real-time detection and tracking of pig body and its different parts. Int J Agric & Biol Eng, 2020; 13(6): 144–149.