Pedestrian Detection Method Based on Recurrent Convolutional Neural Networks

Abstract

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.