Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

Cheng Wang; Hongqian Chen; Xuebin Zhang; Chaoying Meng

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Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

机译：基于混合支持向量机的蛋鸡跟踪算法评估

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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.

机译：背景：行为是反映动物福祉的重要指标。对视频进行手动分析是研究动物行为的最常用方法。但是，这种方法很繁琐，并且要取决于分析人员的主观判断。结果：在本研究中，开发了一种基于混合支持向量机（HSVM）的算法来自动跟踪蛋鸡的蛋鸡使用实验平台在地板系统下饲养的蛋鸡进行了超过500小时的视频。实验结果表明，HSVM跟踪器的性能优于Frag（基于片段的跟踪方法），TLD（跟踪学习-检测，PLS（通过偏最小二乘分析进行目标跟踪），MeanShift算法和基于重叠率和平均值的粒子滤波算法结论：实验结果表明，HSVM追踪器在追踪单个产蛋鸡方面具有比其他测试算法更好的鲁棒性和最先进的性能，具有潜在的监测动物行为的能力。实际饲养条件。

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《畜牧与生物技术杂志(英文版)》 |2017年第1期|226-235|共10页
作者
Cheng Wang; Hongqian Chen; Xuebin Zhang; Chaoying Meng;
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作者单位

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;

Network Center, China Agricultural University, Beijing 100083, China;

College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China;

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;

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正文语种 eng
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Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

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