Moving Object Tracking in Video Sequences based on Energy of Daubechies Complex Wavelet Transform

Object tracking in the video sequence is a challenging task because of its various applications in video compression, video surveillance, robot technology etc. Several object tracking methods exist in spatial and wavelet domain, to work with rigid and non-rigid object boundaries. Spatial domain tracking techniques are not accurate as well as they are slow and this is a major reason why wavelet domain tracking methods are getting popular. Real-valued wavelet transform suffers from shift sensitivity producing inaccurate object tracking. In this paper, we proposed a novel object tracking method using Daubechies Complex Wavelet transform (DaubCxWT). Use of this transform is suitable to track the object from video sequences because of its approximate shift-invariance nature. Tracking of object in the first frame is done by computing the Daubechies complex wavelet coefficients corresponding to the object of interest and then matching energy of these coefficients to the object neighborhood, in Daubechies complex wavelet domain, to perform the tracking in the next consecutive frames. The proposed method needs only complex wavelet coefficients for tracking and hence it is simple in implementation and tracks object efficiently.

[1]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[2]  Manish Khare,et al.  Dual Tree Complex Wavelet Transform Based Video Object Tracking , 2010, ICT.

[3]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[4]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[5]  Gilbert Strang,et al.  Wavelets and Dilation Equations: A Brief Introduction , 1989, SIAM Rev..

[6]  Ramesh A. Gopinath,et al.  The phaselet transform-an integral redundancy nearly shift-invariant wavelet transform , 2003, IEEE Trans. Signal Process..

[7]  C. Sidney Burrus,et al.  A new framework for complex wavelet transforms , 2003, IEEE Trans. Signal Process..

[8]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[9]  Osama Masoud,et al.  A novel method for tracking and counting pedestrians in real-time using a single camera , 2001, IEEE Trans. Veh. Technol..

[10]  J. Lina,et al.  Complex Daubechies Wavelets , 1995 .

[11]  Thomas S. Huang,et al.  Image processing , 1971 .

[12]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[14]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[15]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Mubarak Shah,et al.  Target tracking in airborne forward looking infrared imagery , 2003, Image Vis. Comput..

[17]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[18]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[19]  Ashish Khare,et al.  Tracking of Non-Rigid Object in Complex Wavelet Domain , 2011, J. Signal Inf. Process..

[20]  Demin Wang.  Unsupervised video segmentation based on watersheds and temporal tracking , 1998, IEEE Trans. Circuits Syst. Video Technol..

[21]  Jeffrey Ng,et al.  A steerable complex wavelet construction and its application to image denoising , 2005, IEEE Transactions on Image Processing.