基于Faster R-CNN的人体行为检测研究

Research on human behavior detection based on Faster R-CNN

由于人体行为类内差异大,类间相似性大,而且还存在视觉角度与遮挡等问题,使用人工提取特征的方法特征提取难度大并且难以提取有效特征,使得人体行为检测率较低。针对这个问题,本文在物体检测的基础上使用检测效果较好的Faster R-CNN算法来进行人体行为检测,并对Faster R-CNN算法与批量规范化算法和在线难例挖掘算法进行结合,有效利用了深度学习算法实现人体行为检测。对此改进算法进行实验验证,验证的分类和位置精度达到了80%以上,实验结果表明,改进的算法具有识别精度高的特点。

Because of large intra-class difference and large inter-class similarity of human behaviors,as well as problems such as visual angle and occlusion,it is difficult to extract features,especially effective features,using the manual feature extraction method.This results in low accuracy of human behavior detection.To solve this problem,this paper applies a faster region-based convolutional neural network(Faster R-CNN)algorithm,which has a better detection effect,to detect human behavior on the basis of object detection.By combining the Faster-RCNN algorithm with batch normalization algorithm and an online hard example mining algorithm,the deep learning algorithm is effectively utilized to detect human behavior.Experimental results show that the accuracy of classification and position of the improved algorithm exceeds 80%,thereby verifying its high recognition accuracy.