Efficiency improvement of human body detection with histograms of oriented gradients

In this paper we investigate improvements to the efficiency of human body detection using histograms of oriented gradients (HOG). We do this without compromising the performance significantly. This is especially relevant for embedded implementations in smart camera systems, where the on-board processing power and memory is limited. We focus on applications for indoor environments such as offices and living rooms. We present different experiments to reduce both the computational complexity as well as the memory requirements for the trained model. Since the HOG feature length is large, the total memory size needed for storing the model can become more than 50 MB. We use a feature selection based on Bayesian theory to reduce the feature length. Additionally we compare the performance of the full-body detector with an upper-body only detector. For computational complexity reduction we employ a ROI-based approach.