Int’l Conf. on Computer & Communication Technology │ICCCT’10│ ___________________________________ 978-1-4244-9032-5/10/$26.00©2010 IEEE 230 Curvelet Transform based Object Tracking Swati Nigam and Ashish Khare Department of Electronics and Communication University of Allahabad, Allahabad swatinigam.au@gmail.com, khare@allduniv.ac.in Abstract - In this paper, we have proposed a new object tracking method in video sequences which is based on curvelet transform. The wavelet transform has widely been used for object tracking purpose, but it cannot well describe curve discontinuities. We have used curvelet transform for tracking. Tracking is done using energy of curvelet coefficients in sequence of frames. This method is suitable for object tracking as well as human object tracking purpose also. The proposed method is simple and does not require any other parameter except curvelet coefficients. Experimental results demonstrate performance of this method. Keywords – Object Tracking, Video Sequences, Curvelet Transform. 1. INTRODUCTION The object tracking in video sequences is a very popular problem in the field of computer vision [1] today. Object tracking is the basis of applications in many areas like security and surveillance, clinical applications, biomechanical applications, human robot interaction, entertainment, education, and training etc. Initially, researchers have focused on tracking of a single object, whereas the focus of recent research is on tracking of multiple objects [2]. Various tracking algorithms are described in [3]. It is now well established fact that complex wavelets are one of the most promising tools for object tracking purpose. Complex wavelets are very suitable for representing local features. Several methods exist for object tracking using wavelets [4,6,7,20]. The Dual-Tree Complex Wavelet Transform is an efficient approach and gives better directional selectivity [4]. The important work for object tracking was performed by Khansari et al. [5- 7]. Khansari et al. [5] developed a new noise robust algorithm for tracking the user-defined shapes in noisy images and video sequences by using the features generated in the Undecimated Wavelet Packet Transform (UWPT). They analyzed the adaptation of a feature vector generation and block matching algorithm in the UWPT domain for tracking human objects, in crowded scenes, in presence of occlusion [6] and introduced a new tracking algorithm that can manage partial or short- term full occlusion [7]. However, the wavelet transform does not process edge discontinuities optimally, and discontinuities across a simple edge affect all the wavelets coefficients on the edge. The Ridgelet transform was introduced to overcome the weakness of wavelets in higher dimensions [8]. It provides a good representation for line singularities also in 2D space. Xiao et al. [9] presented a human object tracking system based on the ridgelet transform and proved to be an alternative to wavelet representation of image data. The ridgelet transform is capable of handling one dimensional singularities only. The new tight frame of curvelet [10] is an effective nonadaptive representation for objects with edges [11]. The continuous curvelet transform [12,13] and 3D discrete curvelet transform [14] are well capable of handling two dimensional singularities also. Zhang et al. [15] experimentally confirmed that the use of curvelet transform to extract face features is a more effective approach for object tracking. Lee and Chen [16] used the digital curvelet transform to capture the high dimensional features at different scales and different angles. Also Mandal et al. [17] presented an improvement by reducing the number of coefficients. Extending the previous works done on curvelet transform, in this paper, we propose an implementation of the curvelet transform – the 2D discrete curvelet transform and describe a new method for object tracking using curvelet transform from a video scene. The approach uses energy of curvelet coefficient for tracking of objects. The rest of the paper is organized as follows: section 2 describes basic concepts of curvelet transform. Section 3 deals with the proposed tracking algorithm. Experimental results and conclusions are given in section 4 and 5 respectively. II THE CURVELET TRANSFORM Curvelet Transform is a new multi-scale representation, suitable for objects with curves. It was developed by Candès and Donoho in 1999. Curvelets are designed to handle curves using only a small number of coefficients. Hence the curvelet transform handles curve discontinuities well. The curvelet transform includes four stages: (i) Sub-band decomposition