This paper describes a complete pedestrian detection system based on sliding windows. Two feature vector extraction techniques are used: HOG (Histogram of Oriented Gradient) and CSS (Color Self Similarities), and to classify windows we use linear SVM (Support Vector Machines). Besides these techniques, we use mean shift and hierarchical clustering, to fuse multiple overlapping detections. The results we obtain on the dataset INRIA Person shows that the proposed system, using only HOG descriptors, achieves better results over similar systems, with a log average miss rate equal to 43%, against 46%, due to the cutting of final detections to better adapt them to the modified annotations. The addition of the modified CSS increases the efficiency of the system, leading to a log average miss rate equal to 39%.