Orientation-aware People Detection and Counting Method based on Overhead Fisheye Camera

Abstract

The rise of intelligent vision-based people detection and counting methods will have a significant impact on the future security and space management of intelligent buildings. The current deep learning-based people detection algorithm achieves state-of-the-art performance in images collected by standard cameras. Nevertheless, standard vision approaches do not perform well on fisheye cameras because they are not suitable for fisheye images with radial geometry and barrel distortion. Overhead fisheye cameras can provide a larger field of view compared to standard cameras in people detection and counting tasks. In this paper, we propose an orientation-aware people detection and counting method based on an overhead fisheye camera. Specifically, an orientation-aware deep convolutional neural network with simultaneous attention refinement module (SARM) is introduced for people detection in arbitrary directions. Based on the attention mechanism, SARM can suppress the noise feature and highlight the object feature to improve the context focusing ability of the network on the people with different poses and orientations. Following the collection of detection results, an Internet of Things (IoT) system based on Real Time Streaming Protocol (RTSP) is constructed to output results to different devices. Experiments on three common fisheye image datasets show that under low light conditions, our method has high generalization ability and outperforms the state-of-the-art methods.