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Robust Human Detection using Bag-of-Words and Segmentation

It is reported that Bag-of-Words (BoW) is effective to detect humans with large pose changes and occlusions in still images. BoW can make consistent representation even if a human has pose changes and occlusions. However, the conventional method represents all information within a bounding box as positive data. Since the bounding box is the rectangle including a human, background region is also included in BoW representation. The background region affects BoW representation and the detection accuracy decreases. Thus, in this paper, we propose to segment the region by GrabCut or Color Names, and the influence of background is reduced and we can obtain BoW histogram from only human region. By the comparison with the deformable part model (DPM) and conventional method using BoW, the effectiveness of our method is demonstrated.