基于视觉的四足动物骨架及行走步态特征提取方法

Method for skeleton and gait parameters extraction of quadrupeds walking based on vision

为解决四足动物行走期间的骨架信息及步态特征提取问题,基于HRnet深度神经网络,通过上下文信息增强与多尺度信息融合构建了四足动物骨架的提取模型。在此基础上,建立了基于骨架信息对步态特征进行定量分析的方法。通过在测试数据集上对该模型的有效性进行验证,实验结果表明,该模型具有较好的精度,对四足动物关键点估计的平均相似度为81.04%,准确率为92.77%,召回率为92.75%。基于骨架提取模型,以水牛、羊驼为实验对象,对其行走时的步频特征进行分析计算,实验结果与人工统计结果相比,最大相对误差为2.73%。通过对一个完整步态周期中水牛和羊驼的髋关节和膝关节角度变化规律进行分析,提取了四足动物行走过程中的关节运动逻辑以及步态顺序。最后,以犀牛为实验对象,验证了方法对拍摄角度的变化具有一定适应性。研究结果可为四足动物运动信息的智能感知提供参考。

Due to the need of extracting skeleton information and gait parameters for walking quadruped,a skeleton extraction model of quadruped is proposed by using context information enhancement and multi-scale information fusion based on HRnet,and a quantitative analysis method for gait parameters is established.The validity of the model is verified on the test data set of images.Experiments show that,in the key point estimation of quadruped,the model achieves good performance with the mean similarity of 81.04%,the accuracy of 92.77%,and the recall rate of 92.75%.Based on the skeleton extraction model,the frequency of buffalo and alpaca were analyzed and calculated.Compared with the manual statistical results,the maximum relative error was 2.73%.Through analyzing the angle variations of buffalo’s and alpaca’s hip and knee joints during a complete gait cycle respectively,the joint motion logic and gait sequence of walking quadruped can be extracted automatically.Finally,taking the rhinoceros as a sample,it is demonstrated that the proposed method can work adaptively in a range of different shooting angle of images.The results can provide a reference for intelligent perception of quadruped motion information.