H.264 compressed video classification using Histogram of Oriented Motion Vectors (HOMV)

In this paper, we have proposed a simple and effective approach to classify H.264 compressed videos, by capturing orientation information from the motion vectors. Our major contribution involves computing Histogram of Oriented Motion Vectors (HOMV) for overlapping hierarchical Space-Time cubes. The Space-Time cubes selected are partially overlapped. HOMV is found to be very effective to define the motion characteristics of these cubes. We then use Bag of Features (BOF) approach to define the video as histogram of HOMV keywords, obtained using k-means clustering. The video feature, thus computed, is found to be very effective in classifying videos. We demonstrate our results with experiments on two large publicly available video database.

[1]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[2]  Jason J. Corso,et al.  Action bank: A high-level representation of activity in video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Peter Lambert,et al.  Moving object detection in the H.264/AVC compressed domain for video surveillance applications , 2009, J. Vis. Commun. Image Represent..

[4]  Gang Wei,et al.  Video classification based on HMM using text and faces , 2000, 2000 10th European Signal Processing Conference.

[5]  Gregory D. Hager,et al.  Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions , 2009, CVPR.

[6]  Mubarak Shah,et al.  Recognizing 50 human action categories of web videos , 2012, Machine Vision and Applications.

[7]  Gary J. Sullivan,et al.  Rate-constrained coder control and comparison of video coding standards , 2003, IEEE Trans. Circuits Syst. Video Technol..

[8]  Peng Wang,et al.  A hybrid approach to news video classification multimodal features , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Yannis Avrithis,et al.  Spatiotemporal saliency for video classification , 2009, Signal Process. Image Commun..

[11]  R. Venkatesh Babu,et al.  Recognition of human actions using motion history information extracted from the compressed video , 2004, Image Vis. Comput..

[12]  Steven Verstockt,et al.  Multi-view Object Localization in H.264/AVC Compressed Domain , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[13]  Diane J. Cook,et al.  Automatic Video Classification: A Survey of the Literature , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Zhu Liu,et al.  Integration of multimodal features for video scene classification based on HMM , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).

[15]  S. Shankar Sastry,et al.  High-Speed Action Recognition and Localization in Compressed Domain Videos , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[17]  Thomas Serre,et al.  HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.

[18]  R. Venkatesh Babu,et al.  Video object segmentation: a compressed domain approach , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[20]  Sanjeev R. Kulkarni,et al.  Rapid estimation of camera motion from compressed video with application to video annotation , 2000, IEEE Trans. Circuits Syst. Video Technol..

[21]  R. Venkatesh Babu,et al.  Compressed domain action classification using HMM , 2002, Pattern Recognit. Lett..