Video Monitoring Behaviors of Captive-farmed Porcupines
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摘要: 为了更好地了解豪猪的习性，提高豪猪人工养殖技术水平，本文设计了基于一种视频图像分析的圈养豪猪检测及基本行为识别方案。首先通过混合高斯模型背景建模法，对圈养豪猪养殖环境进行背景建模，标记出场景中的豪猪及其他运动物体轮廓，采用分类算法对识别出的轮廓进行分类，对豪猪的识别准确率达到86.34%；为了进一步提高准确率，引入图像局部特征ORB关键点作为分类属性，使豪猪的识别准确率提升到93.23%；在此基础上，以饲养池结构及豪猪活动实际情况为判断依据建立圈养豪猪行为识别模型，实现了对豪猪静息、进食、饮水、排泄、啃咬铁门及水槽等7种基本行为的识别。Abstract: To understand the living habits for remotely managing the breeding of captive-farmed porcupines, this study applied video to monitor and establish a recognition model with the aid of computation for the behaviors of the animals. Firstly, the mixed Gaussian background modeling was used to build a movement contour model of the porcupines in the pan. Using 3 chosen classifiers, the marked scenes of porcupine activities were categorized with an accuracy of 86.34%. Subsequently, ORB key points were introduced as an additional attribute for the classification which raised the accuracy to 93.23%. The resulting model could now recognize 7 basic behaviors, including resting, eating, drinking, excretion, and chewing an iron gate or a water trough, of porcupines in captivity.
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图 6 4种特征点检测结果对比
注：A为Shi-Tomasi，B为SURF，C为SIFT，D为ORB。
Figure 6. Results of 4 key point detection algorithms
图 7 ORB关键点检测及分组
A为输入图像，B为ORB特征点检测，C为特征点提取，D为特征点分组。
Figure 7. ORB key point detection and classification
表 1 4种特征点算法运行时间
Table 1. Running time of 4 key point detection algorithms
算法 Shi-Tomas SIFT SURF ORB 时间/ms 31416.3 602.262 126.081 20.7 
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表 2 几种分类算法的识别准确率比较
Table 2. Comparison of recognition accuracy among classifiers
(单位/%) 分类算法 1只豪猪 2只豪猪 3只豪猪 平均值 J48 86.10 89.40 72.90 86.43 J48C 92.30 96.00 91.40 93.23 BAYESNET 81.20 89.60 81.40 83.39 BAYESNETC 83.90 94.50 94.30 87.07 C-SVM 85.50 88.10 4.30 82.93 C-SVMC 88.40 84.10 80.00 86.95
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