Real Time Object Tracking in H.264/ AVC Using Polar Vector Median and Block Coding Modes
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33103
Real Time Object Tracking in H.264/ AVC Using Polar Vector Median and Block Coding Modes

Authors: T. Kusuma, K. Ashwini

Abstract:

This paper presents a real time video surveillance system which is capable of tracking multiple real time objects using Polar Vector Median (PVM) and Block Coding Modes (BCM) with Global Motion Compensation (GMC). This strategy works in the packed area and furthermore utilizes the movement vectors and BCM from the compressed bit stream to perform real time object tracking. We propose to do this in view of the neighboring Motion Vectors (MVs) using a method called PVM. Since GM adds to the object’s native motion, for accurate tracking, it is important to remove GM from the MV field prior to further processing. The proposed method is tested on a number of standard sequences and the results show its advantages over some of the current modern methods.

Keywords: Block coding mode, global motion compensation, object tracking, polar vector median, video surveillance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021717

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 748

References:


[1] A. Borji and L. Itti. State-of-the-art in visual attention modeling. IEEE Trans.Pattern Anal. Mach. Intell., 35(1):185-207, 2013. 2, 5, 9, 24, 25
[2] V. Mahadevan and N. Vasconcelos. Spatiotemporal saliency in dynamic scenes. IEEE Trans. Pattern Anal. Mach. Intell., 32(1):171-177, 2010. 35, 41, 61, 93, 88.
[3] Z. Liu, H. Yan, L. Shen, Y.Wang, and Z. Zhang. A motion attention model based rate control algorithm for H. 264/AVC. In The 8th IEEE/ACIS International Conference on Computer and Information Science (ICIS'09).
[4] T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the H. 264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol.
[5] S. H. Khatoonabadi and I. V. Bajic. Still visualization of object motion in compressed video. In Proc. IEEE ICME'13 Workshop: MMIX, 2013.
[6] G. Abdollahian, Z. Pizlo, and E.J. Delp. A study on the effect of camera motion on human visual attention. In Proc. IEEE ICIP'08, pages 693-696, 2008.
[7] Tracking database for standard video sequences. http://www.sfu.ca/~ibajic/ datasets.html.
[Online]..
[8] Y. M. Chen, I. V. Baji´c, and P. Saeedi. Motion segmentation in compressed video using Markov random fields. In Proc. IEEE ICME’10, pages 760–765, 2010. 96.
[9] A. Smolic, M. Hoeynck, and J. R. Ohm. Low-complexity global motion estimation from P-frame motion vectors for MPEG-7 applications. In Proc. IEEE ICIP'00,volume 2, pages 271-274, 2000..
[10] W. Zeng, J. Du, W. Gao, and Q. Huang. Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imaging, (11):290–299, 2005.
[11] M. G. Arvanitidou, A. Glantz, A. Krutz, T. Sikora, M. Mrak, and A. Kondoz. Global motion estimation using variable block sizes and its application to object segmentation. In Proc. IEEE WIAMIS'09, pages 173{176, 2009. 67, 104
[12] J. Astola, P. Haavisto, and Y. Neuvo. Vector median filters. Proceedings of the IEEE,78(4).
[13] 264/AVC reference software. http://iphome.hhi.de/suehring/tml/. (Online).