Performance Analysis of Alpha Beta Filter, Kalman Filter and Meanshift for Object Tracking in Video Sequences

摘要

Object Tracking is becoming increasingly important in areas of computer vision, surveillance, image processing and artificial intelligence. The advent of high powered computers and the increasing need of video analysis has generated a great deal of interest in object tracking algorithms and its applications. This said it becomes even more important to evaluate these algorithms to quantify their performance. In this paper, we have implemented three algorithms namely Alpha Beta filter, Kalman filter and Meanshift to track an object in a video sequence and compared their tracking performance based on various parameters in normal and noisy conditions. The proposed parameters employed are error plots in position and velocity of the object, Root mean square error, object tracking error, tracking rate and time taken to track the object. The goal is to illustrate practically the performance of each algorithm under such conditions quantitatively and identify the algorithm that performs the best. Reference [1]Alper Yilmaz, Omar Javed, Mubarak Shah "Object Tracking: A Survey" in ACM Computing Survey Volume 38, Dec 2006, pg-2 [2]Abhishek Kumar Chauhan, Deep Kumar "Study of Moving Object Detection and Tracking of Video Surveillance" in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013. [3]D.M Akbar Hussain, David Hicks, Daniel Orti Arroyo "Case Study: Kalman and Alpha Beta Computation under High Correlation" in Proceedings of International Multi-Conference of engineers and computer scientists Vol I, March2008. [4]M. Munu Harrison and M.S. Woolfson, Comparison of the Kalman and α-β Filters for the Tracking of Targets Using Phased Array Radar,University of Nottingham, U.K,Vol 4,2012 [5]Jae-Chern Yoo, Young-Soo Kim. Alpha–beta-tracking index tracking filter, In POSTECH Information Research Lab, Pohang University of Science and Technology, South Korea, Vol 5 2003 [6]Ramsey Faragher, Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation, In IEEE Signal Processing Magazine, September 2012. [7]Greg Welch, George Bishop "An Introduction to the Kalman filter" Sep 1997, pg 1 -6. [8]Hitesh A Patel, Darshak G Thakore "Moving Object Tracking Using Kalman Filter" IJCSMC, Vol. 2, Issue. 4, April 2013, pg.326 – 332 [9]Zhaoxia Fu, Yan Han. Centroid weighted Kalman filter for visual object tracking, In Information and Communication Engineering Institute, North University of China, Taiyuan, Elviewer 2012. [10]Dorin Comaniciu, Peter Meter "Meanshift: A robust approach towards feature space analysis" IEEE transactions on pattern analysis and macine intelligence ol 24, May 2002. [11]Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, l7 (8): pg-790-799, 1998. [12]Faisal Bashir, Fatih Porikli "Performance evaluation of object detection and Tracking systems" in Mitsubishi Electric Research Laboratories June 2006. [13]Fei Yin Dimitrios Makris, Sergio Velastin " Performance evaluation of object tracking algorithms" in Digital Imaging Research center. [14]V Purandhar Reddy,K Thirumala Redd, YB. Dawood "Performance evaluation of object tracking technique based on position vectors", International journal of image processing Vol 7, Issue 2 2013.