IEEE-ICET 2006 2 nd International Conference on Emerging Technologies Peshawar, Pakistan, 13-14 November 2006 1-4244-0502-5/06/$20.00©2006 IEEE 174 Object Tracking using Correlation, Kalman Filter and Fast Means Shift Algorithms Ahmad Ali 1 , Dr. Sikander Majid Mirza 2 1,2 Pakistan Institute of Engineering & Applied Sceinces,Islamabad,Pakistan. 1 ahmadali1655@hotmail.com , 2 sikander_majid@yahoo.com Abstract: Object detection in videos involves verifying the presence of an object in image sequences and possibly locating it precisely for recognition. Object tracking is to monitor an object’s spatial and temporal changes during a video sequence, including its presence, position, size, shape, etc. This is done by solving the temporal correspondence problem, the problem of matching the target region in successive frames of a sequence of images taken at closely-spaced time intervals. These two processes are closely related because tracking usually starts with detecting objects, while detecting an object repeatedly in subsequent image sequence is often necessary to help and verify tracking. In this paper, a novel approach is being presented for object tracking. It includes combination of 2D normalized correlation, Kalman filter and fast mean shift algorithm. 1. INTRODUCTION Videos are actually sequences of images, each of which called a frame, displayed in fast enough frequency so that human eyes can percept the continuity of its content. It is obvious that all image processing techniques can be applied to individual frames. Besides, the contents of two consecutive frames are usually closely related. Visual content can be modeled as a hierarchy of abstractions. At the first level are the raw pixels with color or brightness information. Further processing yields features such as edges, corners, lines, curves, and color regions. A higher abstraction layer may combine and interpret these features as objects and their attributes. At the highest level are the human level concepts involving one or more objects and relationships among them. Object detecting and tracking has a wide variety of applications in computer vision such as video compression, video surveillance, vision-based control, human-computer interfaces, medical imaging, augmented reality, and robotics. Additionally, it provides input to higher level vision tasks, such as 3D reconstruction and 3D representation. It also plays an important role in video database such as content-based indexing and retrieval. This paper describes a novel way for tracking of a general purpose object which is fail-safe. 2. TRACKING ALGORITHM In a very abstract way, tracking algorithm can be described by following diagram. Fig.1 Tracking Algorithm The core of the algorithm is to search the target of interest (TOI) in the image frame and the replacement of template by the target found in current image frame, called template-updating stage. A. Template Searching To develop fail-safe tracking algorithm, first template matching technique was tested. Normalized 2D correlation based method was used for template matching.[2] In every next frame, template was matched, if correlation is greater than threshold value then it is assumed that target is detected. After confirmation of target, template is updated with new detected target. This updating of template helps to cater the problem of any change in target shape or Searching of Target of Interest (TOI) Updating of Template Image Frame