Object Visual Tracking Using Window-Matching Techniques and Kalman Filtering

Abstract

Object visual tracking aims to determine the image configuration of a target region of an object as it moves through a camera's field of view. The visual tracking process consists on matching the target region in successive frames of a sequence of images taken at closely-spaced intervals. Visual tracking has become an important process on various applications as: vision-based control (Hutchinson et al., 1996; Papanikolopoulos et al., 1992), industrial robotics (Sumi et al., 2007), biomedicine (Shen et al., 2006), surveillance (Urtasun et al., 2006), aerial target tracking (Yau et al., 2001), aircraft and car traffic monitoring and control (Rostamianfar et al., 2006). Algorithms that combine digital image processing and visual servo control techniques are being applied to the solution of complex problems such as object tracking from a sequence of images (Hager et al., 1998). Visual tracking can be considered an estimation process acting together with digital image processing techniques. For the estimation process a stochastic filtering approach using Kalman filter can be applied (Veeraraghavan et al., 2006) and the particle filter (Shen et al., 2006). A visual tracking algorithm in (Babu et al., 2007) combines mean-shift tracker with a modified window-matching algorithm in order to avoid drift during partial object occlusion. Other algorithm (Brassnet et al., 2007) uses particle filtering for object tracking based on multiple cues with adaptive parameters and its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. These estimation approaches can be applied to visual servo control in association with window-matching techniques yielding better results (Tan et al., 2005). Here, an object tracking algorithm is proposed that combines the window-matching techniques and optimal estimation theory based on the linear stochastic Kalman filtering (Kalman, 1960). The window-matching algorithm is modified and a Kalman filtering stage is coupled to improve the tracking performance. The main objective of this work was to develop the structure of a tracking algorithm not yet its final and efficient implementation, so it was developed within the Matlab computational environment. The chapter is organized as follows, in Section 2 the object visual tracking problem is stated together with the solving methods. Section 3 discusses the window-matching techniques and presents a window-matching algorithm (WM) for tracking purposes. Section 4 deals with the use of the Kalman filtering (K) to improve the object visual tracking, a new algorithm (WM+K) is then presented. Section 5 then shows the application of the WM+K