Human Detection in Video over Large Viewpoint Changes

In this paper, we aim to detect human in video over large viewpoint changes which is very challenging due to the diversity of human appearance and motion from a wide spread of viewpoint domain compared with a common frontal viewpoint. We propose 1) a new feature called Intra-frame and Inter-frame Comparison Feature to combine both appearance and motion information, 2) an Enhanced Multiple Clusters Boost algorithm to co-cluster the samples of various viewpoints and discriminative features automatically and 3) a Multiple Video Sampling strategy to make the approach robust to human motion and frame rate changes. Due to the large amount of samples and features, we propose a two-stage tree structure detector, using only appearance in the 1 stage and both appearance and motion in the 2 stage. Our approach is evaluated on some challenging Real-world scenes, PETS2007 dataset, ETHZ dataset and our own collected videos, which demonstrate the effectiveness and efficiency of our approach.