Robust Human Detection to Pose and Occlusion Using Bag-of-Words

To understand the human action in still images, it is effective to detect the human region. However, since appearance of human is much different due to pose and occlusion, the detection is quite difficult. Here we propose robust human detection method to pose and occlusion using Bag-of-Words (BoW). In general, the location information is helpful in classification. When the human has occlusion and pose changes, the location information makes the feature vector inconsistent. By using BoW which ignores the location information, we can obtain the consistent feature representation even if the local feature appears in different location by pose changes. Furthermore, when the part of human is occluded, BoW can also construct the feature representation from only visible part. Thus, BoW makes feature representation robust to partial occlusion and pose changes. By the comparison with deformable part model (DPM), the effectiveness of our method is demonstrated.