Improved Relational Feature Model for People Detection Using Histogram Similarity Functions

In this paper, we propose a new approach for people detection using a relational feature model (RFM) in combination with histogram similarity functions such as the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square χ² histogram similarity function. The relational features are computed for all combinations of extracted features from a feature detection algorithm such as the Histograms of Oriented Gradients (HOG) feature descriptor. 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 our results show, that in addition to less false positives, true positive responses in regions near people are much more accurate using the relational features compared to non-relational feature models.