A Novel System for Video Retrieval of Frontal-View Indoor Moving Pedestrians

This paper presents a solution for video retrieval of frontal-view indoor moving pedestrians. A novel and effective system which contains two parts, feature extraction and key frame sets matching, is proposed. For the first part, a successful fusion strategy is proposed for effectively combining information from color and texture features. The experiment indicates that the retrieval accuracy based on this fusion method is better than only using either of color feature or texture feature. In another part of key frame sets matching, this paper tries to solve the problem from the view of the subspace method. The latest and novel subspace method of Discriminative Canonical Correlations (DCC) is adopted. In the experiment, compared with other two classical subspace methods (MSM and CMSM), the DCC method distinctly outperforms them in terms of retrieval accuracy.