基于递归卷积神经网络的行人检测方法

Pedestrian Detection Method Based on Recurrent Convolutional Neural Networks

为了提高视频中行人检测的准确度,提出了一种基于递归卷积神经网络的行人检测方法.该方法利用递归卷积神经网络融合视频中连续图像的上下文信息,以实现准确的行人检测.首先,利用卷积神经网络提取连续图像的多个特征图组;然后,根据先后次序,将多个特征图输入到递归卷积神经网络中,形成一张关于行人位置的掩码图;最后,通过在掩码图上预测行人的检测框,获得视频中当前图像的行人检测结果.实验结果表明:相比于其他行人检测方法,该方法在ETH、CUHK和PETS 2007三个数据集上都取得较准确的行人检测结果.

To improve the accuracy of pedestrian detection in videos,a pedestrian detection method based on a recurrent convolutional neural network was proposed.The method used the recurrent convolutional neural network to combine the context information among several consecutive images in the video for implementing accurate pedestrian detection.Firstly,a convolutional neural network was used to extract several groups of feature maps of corresponding input images.Secondly,according to the image order,these groups of feature maps were successively input to the recurrent convolutional neural network to form a mask indicating the pedestrian locations.Finally,the pedestrian detection result of the current image was obtained by estimating the bounding boxes on the mask.The experimental results showed that the proposed method can achieve the accurate pedestrian detection results on three datasets(ETH,CUHK,PETS 2007)when compared with other pedestrian detection methods.