Behavior recognition of model animals such as mice, rats, and monkeys, is of great significance in medical research and vital in the development in medicines or drugs. Studies of the animal’s responses to specific stimulations consisted of visual imaging methods that was easily affected by cage lighting conditions and shooting angle. In this study, we designed a behavior classification system that focused on object-floor contact features extracted from touch images of the frustrated total internal reflection injected with infrared lights. A mouse behavioral dataset (IMBD) was then established, and a support vector machine was applied to conduct this classification. To improve its performance, an improved particle swarm optimization was proposed for the optimization of parameters. Based on IMBD and the support vector machine, we tested different feature extraction methods and classifiers. The system performance of the current study showed more accuracy and efficacy compared with performance of other prevalent particle swarm optimizations. Our study resulted in a recognition rate of up to 94.37% for individual behavior. The average rate of behavioral recognition for all tested mice reached 83.09%. The results suggested that foot contact features of mice were more effective than regular video features in behavior recognition.

小鼠、大鼠、猴等模型动物的行为识别在医学研究中具有重要意义，对药物或药物的开发具有重要意义。动物对特定刺激反应的研究包括视觉成像方法，这些方法很容易受到笼子照明条件和拍摄角度的影响。在这项研究中，我们设计了一个行为分类系统，专注于从注入红外光的受抑全内反射的触摸图像中提取的物体-地板接触特征。然后建立鼠标行为数据集（IMBD），并应用支持向量机进行分类。为了提高其性能，提出了一种改进的粒子群优化算法来优化参数。基于IMBD和支持向量机，我们测试了不同的特征提取方法和分类器。与其他流行的粒子群优化的性能相比，当前研究的系统性能显示出更高的准确性和有效性。我们的研究导致对个人行为的识别率高达 94.37%。所有测试小鼠的平均行为识别率达到83.09%。结果表明，小鼠的足部接触特征在行为识别方面比常规视频特征更有效。09%。结果表明，小鼠的足部接触特征在行为识别方面比常规视频特征更有效。09%。结果表明，小鼠的足部接触特征在行为识别方面比常规视频特征更有效。