title: Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine.
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.
authors: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083 China.
authors: Cheng
authors: C
authors: Wang
authors: Network Center, China Agricultural University, Beijing, 100083 China ; College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083 China.
authors: Hongqian
authors: H
authors: Chen
authors: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083 China.
authors: Xuebin
authors: X
authors: Zhang
authors: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083 China.
authors: Chaoying
authors: C
authors: Meng
keywords: Computer vision; Laying hens; Locomotion tracking; Support vector machine