Abstract
In this paper, we propose a method for the human detection. This method use similarity feature as our feature and hough forest as our classifier. We calculate the similarity between input path and example patch, and this similarity is one bin of our similarity feature. This similarity feature makes the split of the node in hough forest more meaningful. Through the hough forest, it collect the similar patch in the same leaf node, so that the leaf node can be seen as the part detector. Besides the leaf node also severs as a codebook recording the possible locations of the human center. Based on the codebook, the patch falling in this leaf node can cast probabilistic votes for possible locations of the centroid of the human. After all the path cast their votes, more votes indicate more likely the centroid of the human is. In order to reduce the error voting, we use the sliding window strategy to prevent casting the votes outside the window. The local maximum in the hough space of the window is then filtered by the threshold. The position with remaining local maximum is then transformed to the coordinate of the image, and cast the second vote in the hough space of the original image. By using the sliding window strategy, the filter of the threshold, and the second vote method, it can reduce the false positive in the image.