Human detection from low-resolution video images using 3D convolutional neural network

In this paper, we propose a method for human detection from low-resolution camera images. The proposed method uses video images as input and uses 3D-CNN for classification, which is an extension of 2D-CNN and that can take into account temporal features such as gait motion. In our experiments, we used Caltech Pedestrian Detection Benchmark to make datasets of low-resolution still and video images and compared the performance between 2D-CNN and 3D-CNN. As a result, 3D-CNN with low-resolution video images achieved 91.8 % accuracy rate, 99.0 % precision rate, and 82.8 % recall rate, and showed higher performance than 2D-CNN with low-resolution images, and comparable performance than 2D-CNN with high-resolution images.