Moving pedestrian detection based on motion segmentation

The detection of moving pedestrians is of major importance in the area of robot vision, since information about such persons and their tracks should be incorporated into reliable collision avoidance algorithms. In this paper, we propose a new approach, based on motion analysis, to detect moving pedestrians. Our main contribution is to localize moving objects by motion segmentation on an optical flow field as a preprocessing step, so as to significantly reduce the number of detection windows needed to be evaluated by a subsequent people classifier, resulting in a fast method for real-time systems. Therefore, we align detection windows with segmented motion-blobs using a height-prior rule. Finally, we apply a Histogram of Oriented Gradients (HOG) features based Support Vector Machine with Radial Basis Function kernel (RBF-SVM) to estimate a confidence for each detection window, and thereby locate potential pedestrians inside the segmented blobs. Experimental results on "Daimler mono moving pedestrian detection" benchmark show that our approach obtains a log-average miss rate of 43% in the FPPI range [10-2, 100], which is a clear improvement with respect to the naive HOG+linSVM approach and better than several other state-of-the-art detectors. Moreover, our approach also reduces runtime per frame by an order of magnitude.