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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 376494, 12 pages
Research Article

Robust Object Tracking Based on Simplified Codebook Masked Camshift Algorithm

1Information Research Institute, Shandong Academy of Sciences, Jinan 250014, China
2Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Information Science and Engineering, Shandong University, Jinan 250100, China

Received 26 January 2015; Revised 5 June 2015; Accepted 10 June 2015

Academic Editor: Fernando Torres

Copyright © 2015 Yuanyuan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Moving targets detection and tracking is an important and basic issue in the field of intelligent video surveillance. The classical Codebook algorithm is simplified in this paper by introducing the average intensity into the Codebook model instead of the original minimal and maximal intensities. And a hierarchical matching method between the current pixel and codeword is also proposed according to the average intensity in the high and low intensity areas, respectively. Based on the simplified Codebook algorithm, this paper then proposes a robust object tracking algorithm called Simplified Codebook Masked Camshift algorithm (SCMC algorithm), which combines the simplified Codebook algorithm and Camshift algorithm together. It is designed to overcome the sensitiveness of traditional Camshift algorithm to background color interference. It uses simplified Codebook to detect moving objects, whose result is employed to mask color probability distribution image, based on which we then use Camshift to predict the centroid and size of these objects. Experiment results show that the proposed simplified Codebook algorithm simultaneously improves the detection accuracy and computational efficiency. And they also show that the SCMC algorithm can significantly reduce the possibility of false convergence and result in a higher correct tracking rate, as compared with the traditional Camshift algorithm.