Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Wang, Cheng; Chen, Hongqian; Zhang, Xuebin; Meng, Chaoying
• Is Part Of: Journal of animal science and biotechnology. Volume 7:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Computer vision -- Laying hens -- Locomotion tracking -- Support vector machine; Animal biotechnology -- Periodicals; Animal culture -- Periodicals; Domestic animals -- Periodicals; Livestock -- Periodicals; 636.08
• DOI: 10.1186/s40104-016-0119-3
• Languages: English
• ISSNs: 2049-1891

Abstract

Abstract Background Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem. Results In this study, an algorithm based on a Hybrid Support Vector Machine (HSVM) was developed for the automated tracking of individual laying hens in a layer group. More than 500 h of video was conducted with laying hens raised under a floor system by using an experimental platform. The experimental results demonstrated that the HSVM tracker outperformed the Frag (fragment-based tracking method), the TLD (Tracking-Learning-Detection), the PLS (object tracking via partial least squares analysis), the MeanShift Algorithm, and the Particle Filter Algorithm based on their overlap rate and the average overlap rate. Conclusions The experimental results indicate that the HSVM tracker achieved better robustness and state-of-the-art performance in its ability to track individual laying hens than the other algorithms tested. It has potential for use in monitoring animal behavior under practical rearing conditions.