Pedestrian detection based on improved Random Forest in natural images

An approach toward pedestrian detection applied to natural images using improved Random Forest (RF) is proposed. We take a more discriminative method for object part detection by applying the feature of pixel-based. We firstly train a pedestrian random forest which directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. For a testing image from the TUD dataset, our system requires four operations, which are feature extracting, passing patches through the trees, casting the votes, and processing the Hough images. Experimental results with the challenging TUD Image database demonstrate that the accuracy and robustness of our algorithm are better than those of ISM-based detection method, and this method is promising for object detection significantly.