Pedestrian Detection Utilizing Gradient Orientation Histograms and Color Self Similarities Descriptors

This paper presents a pedestrian detection system in non-controlled environments based on sliding windows. Systems of this type are based on two major blocks: one for feature extraction and other for window classification. Two techniques for feature extraction are used: HOG (Histogram of Oriented Gradient) and CSS (Color Self Similarities), and to classify windows we use linear SVM (Support Vector Machines). Beyond these techniques, we use mean shift and hierarchical clustering to fuse multiple overlapping detections. To improve the system performance, each descriptor is separately classified using an assemble of SVMs. The results obtained on the dataset INRIA Person show that the proposed system, using only HOG descriptors, achieves better results over similar systems. These results were possible due to the cutting of the final detections to better adapt them to the modified annotations, and some modifications on the parameters of the descriptors. The addition of the modified CSS descriptor to the HOG descriptor increases the efficiency of the system, leading to a log average miss rate equal to 36.2%, when classifying each descriptor separately.