Robust Object Tracking Based on Simplified Codebook Masked Camshift Algorithm 。 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。80308

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。

1。Introduction

Moving object detection and tracking is the basis of object recognition and behavior understanding and has very broad application and research prospects。 There are mainly three different categories of object detection algorithms, such as interframe difference methods [1], optical flow meth- ods [2], and background subtraction methods。 Background subtraction methods are the most popular ones in real world because of their high detection accuracy and medium computational complexity。 Classical background subtraction algorithms include kernel density estimation [3], Gaussian Mixture Background Modeling [4], and Codebook back- ground modelling [5]。

Codebook algorithm was first proposed in 2004 by Kim et al。 [5], and it has been one of the most advanced motion detection methods because of its high memoryutilization, high computation efficiency, and strong robust- ness。 Many improvements have been made based on Code- book algorithm。 For example, Wu and Peng [6] proposed a modified Codebook algorithm based on spatiotemporal context which improves the detection accuracy by adding the correlation of the spatiotemporal pixels。 However, the computational complexity of the whole algorithm has been increased at the same time。 Tu et al。 [7] made simplifications to accelerate the computational speed by introducing box- based Codebook model in RGB space to represent the match- ing field of the codewords。 However, these simplifications decreased the detection accuracy。 Most of the improvements to Codebook can improve either the detection accuracy or computational efficiency, but not both of them。

Camshift algorithm is a classical object tracking algo- rithm。 Camshift is evolved from Mean Shift algorithm。 It performs tracking according to the color information ofan object and has very good real-time performance and high robustness。 Mean Shift algorithm was first proposed in 1975 by Fukunaga and Hostetler [8]。 Cheng [9] expended the algorithm and enlarged its application range。 After that, Comaniciu and Meer [10] successfully applied it to image segmentation and object tracking。 Bradski [11] established Camshift algorithm based on Mean Shift, which cannot only predict the centroid position of an object but also adaptively alter the size of an object frame。 Currently, the improvement on Camshift algorithm exists in the following aspects: to improve the accuracy by improving the features of a histogram [12–14], to reduce computation time by increasing convergence velocity [15, 16], to increase robust- ness for objects rotation [17], and to solve the problem of background color interference。 The improvement for Camshift algorithm in this paper concentrates on the issue of background color interference。 In the literature, Camshift and Kalman combined algorithm [18–20] is easy to fail when object movement is nonlinear。 The tracking accuracy of Camshift and interframe difference combined algorithm [21, 22] could be affected by a low performance interframe difference motion detection algorithm。