Identification of Abnormal Gait of Pigs Based on Video Analysis
Gait Analysis has become a new research field in computer vision. So far, however, contributions to this topic almost exclusively considered the problem of person identification. This study describes an automated algorithm that classifies pigs abnormal gait by utilizing video analysis. The classification algorithm consists of three stages: i) Detection and extraction of the moving pig body and its contour from image sequences; ii) Modeling of pigs forelimb and Extraction of gait information by the joint angles and body points; and iii) Motion analysis and feature extraction for classifying abnormal gait. Eigenvectors were extracted by Fourier analysis on the angle sequence. Then, Support Vector Machine (SVM) classifier is applied to classify normalabnormal gait. The algorithm was tested on a set of 58 video fragments. The average classification rate was about 90％.
pig video analysis gait stick model SVM
Zhu Weixing Zhang Jin
School of Electrical and Information Engineering, Jiangsu University Zhenjiang, China
国际会议
2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)
武汉
英文
394-397
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）