Pedestrian detection in outdoor images using color and gradients

Pedestrian Detection in digital images is a task of huge importance for the development of autonomous systems and for the improvement of robots interacting with their environment. The challenges such a system has to overcome are the high inter-class variance of pedestrians and the demands of unstructured environments. Outdoor environments contain unknown regions, inhomogeneous illumination, and parts of the pedestrians can be occluded. In this work, a complete system for pedestrian detection is realized according to state-of-the-art techniques. As main features, we use the “Histograms of Oriented Gradients” in combination with the “Color Self-Similarity” feature as proposed by Walk et al. We describe and evaluate our complete detection approach and our new structure element is able to accelerate the Color Self-Similarity computations by a factor of four.