In this report, an unsupervised pedestrian detection algorithm is proposed. An input image is first divided into overlapping detection windows in a sliding fashion and histogram of oriented gradients (HOG) features are collected over each window using non-overlapping cells. A distance metric is used to determine the distance between histograms of corresponding cells in each detection window and the average pedestrian HOG template (determined a priori). The distance feature vectors over overlapping blocks of cells are concatenated to form the distance feature vector of a detection window. Each window provides a data sample that is extracted from the whole image and then modeled as a normalcy class using support vector data description (SVDD). Assuming that most of the image is covered by background, the outliers that are detected during the modeling of the normalcy class can be hypothesized as detection windows that contain pedestrians in them. The detections are obtained at different scales in order to account for the different sizes of pedestrians. The final pedestrian detections are generated by applying non-maximal suppression on all the detections at all scales. The system is tested on the INRIA pedestrian dataset and its performance analyzed with respect to accuracy and detection rate.
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