Published in IET Computer Vision Received on 7th February 2011 Revised on 19th July 2011 doi: 10.1049/iet-cvi.2011.0023 ISSN 1751-9632 Curvelet transform-based technique for tracking of moving objects S. Nigam A. Khare Department of Electronics and Communication, University of Allahabad, Allahabad, India E-mail: swatinigam.au@gmail.com; ashishkhare@hotmail.com Abstract: This study provides an object tracking method in video sequences, which is based on curvelet transform. The wavelet transform has been widely 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. The proposed method is simple and does not rely on any other parameter except curvelet coefficients. Compared with a number of schemes like Kalman filter, particle filter, Bayesian methods, template model, corrected background weighted histogram, joint colour texture histogram and covariance-based tracking methods, the proposed method extracts effectively the features in target region, which characterise better and represent more robustly the target. The experimental results validate that the proposed method improves greatly the tracking accuracy and efficiency than traditional methods. 1 Introduction Object tracking in video sequences is a very popular problem in the field of computer vision [1]. Object tracking is a process of locating a moving object (or multiple objects) over time using a single camera or multiple cameras [2]. Its objective is to associate target objects in consecutive video frames. Object tracking is the basis of applications in many areas like security, surveillance, clinical applications, biomechanical applications, human robot interaction, entertainment, education, training and so on. There are two key steps in object tracking process: 1. Object detection: detection of an object in a given scenario 2. Object tracking: frame by frame tracking of object Tracking of moving objects is a complicated task due to the following reasons: 1. The object’s shape and size may vary from frame to frame 2. Object may be occluded by other object(s) 3. Presence of noise and blur in video 4. Luminance and intensity changes 5. Object’s abrupt motion 6. Real-time scene analysis requirements Therefore real-time object tracking is a critical task in computer vision applications and tracking of moving object is a topic of great interest for researchers. In order to perform object tracking in video sequences, an algorithm analyses sequential video frames and outputs the movement of target between the frames. Many tracking algorithms have been proposed so far, for this purpose. A good survey of tracking algorithms is provided by Yilmaz et al. [3]. The mean shift algorithm was originally proposed by Fukunaga and Hostetler [4] for data clustering. It was later modified by Cheng [5]. Bradski [6] again modified it and developed a continuously adaptive mean shift algorithm to track a moving face. Another class of mean shift tracking algorithms is based on Kalman filter [7]. In another technique by Nummiaro et al. [8], a bootstrap particle filter was used to sample the observation model. Zivkovic et al. [9] used an efficient local search scheme to find the likelihood of object region and approximated this region by using Bayesian filtering. Shen et al. [10] built a robust template model from a large amount of data instead of single image for tracking. The major drawback of these techniques is to handle the scale changes. The scale selection is a big concern in case of mean shift tracking. If the scale is too big or too small, then it results into poor localisation. Owing to this reason mean shift tracking algorithms are not much promising for object tracking purpose because one cannot chose an exactly correct size of scale for changing situations. A widely used form of object tracking algorithms uses histograms. Lee and Kang [11] developed an area weighted centroid shifting algorithm that takes the colour histograms into account according to the area they cover in the initial target region and contains more spatial information about the distribution of the colours in the target than the original mean shift-based tracking. Colour histogram is an estimating mode of point sample distribution and is very robust in representing the object appearance. However, using only colour histograms in mean shift tracking has some problems. First, the spatial information of the target is lost. Second, when the target has similar appearance to the background, colour histogram will become invalid to distinguish them. The idea of combining colour and texture IET Comput. Vis., 2012, Vol. 6, Iss. 3, pp. 231–251 231 doi: 10.1049/iet-cvi.2011.0023 & The Institution of Engineering and Technology 2012 www.ietdl.org