New approach for human detection in spherical images

Abstract

Omnidirectional cameras are commonly used in computer vision and robotics. Their main advantage is their wide field of view which allows them to acquire a 360 degree view of the scene with only one sensor and a single shot. However, few studies have investigated the human detection problem using this kind of cameras. In this paper, we propose to extend the conventional approach for human detection in perspective images and based on Histogram of Oriented Gradients (HOG) apdapted to spherical images is used for this issue. Our approach uses the Riemannian manifolds in order to adapt the gradient in the omnidirectional images. Several experiments have been done using INRIA image database; the results show that adapting detection and image database to the geometry of omnidirectional camera allows a robust detection, and significantly increases the performances.