People detection in heavy machines applications

In this paper we focus on improving the performance of people detection algorithm on fish-eye images in a safety system for heavy machines. Fish-eye images give the advantage of a very wide angle-of-view, which is important in the context of heavy machines. However, the distortions in fish-eye images present many difficulties for image processing. The underlying framework of the proposed detection system uses Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). By analyzing the effect of distortions in different regions in the field-of-view and by adding artificial distortions in the training process of the binary classifier, we can obtain better detection results on fish-eye images.