Performance evaluation of an improved relational feature model for pedestrian detection

In this paper, we evaluate a new algorithm for pedestrian detection using a relational feature model (RFM) in combination with histogram similarity functions. For histogram comparison, we use the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square χ2 histogram similarity function. Relational features using the HOG descriptor compute the similarity between histograms of the HOG descriptor. The features are computed for all combinations of extracted histograms from a feature detection algorithm. Our experiments show, that the information of spatial histogram similarities reduces the number of false positives while preserving true positive detections. The detection algorithm is done, using a multi-scale overlapping sliding window approach. In our experiments, we show results for different sizes of the cell size from the HOG descriptor due to the large size of the resulting relational feature vector as well as different results from the mentioned histogram similarity functions. Additionally, the results show the influence of the amount of positive example images and negative example images during training on the classification performance of our approach.