2020/05/27 by Guang Chen, Shiwen Shen, Chen, Guang +8 · 1 citation
Computer Science · Engineering · Veterinary · #Animal Behavior and Welfare Studies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Industrial Vision Systems and Defect Detection #Robotics (cs.RO) #Video Surveillance and Tracking Methods #cs.CV #cs.RO #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.13131
IEEE International Conference on Robotics and Automation (ICRA) 2020
arxiv created 2020/05/27 · openalex publication_date 2020/05/27 · arxiv updated 2020/05/28 · openalex created_date 2020/06/05 · openalex updated_date 2026/08/04
Pig counting is a crucial task for large-scale pig farming, which is usually completed by human visually. But this process is very time-consuming and error-prone. Few studies in literature developed automated pig counting method. Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig movements, occlusion and overlapping. Especially, the field of view of a single image is very limited, and could not meet the requirements of pig counting for large pig grouping houses. To that end, we presented a real-time automated pig counting system in crowds using only one monocular fisheye camera with an inspection robot. Our system showed that it produces accurate results surpassing human. Our pipeline began with a novel bottom-up pig detection algorithm to avoid false negatives due to overlapping, occlusion and deformation of pigs. A deep convolution neural network (CNN) is designed to detect keypoints of pig body part and associate the keypoints to identify individual pigs. After that, an efficient on-line tracking method is used to associate pigs across video frames. Finally, a novel spatial-aware temporal response filtering (STRF) method is proposed to predict the counts of pigs, which is effective to suppress false positives caused by pig or camera movements or tracking failures. The whole pipeline has been deployed in an edge computing device, and demonstrated the effectiveness.