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http://dx.doi.org/10.12673/jant.2019.23.1.77

Object Tracking using Color Histogram and CNN Model  

Park, Sung-Jun (School of Electronics and Information Engineering, Korea Aerospace University)
Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.23, no.1, 2019 , pp. 77-83 More about this Journal
Abstract
In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.
Keywords
CNN; GOTURN; Mean-shift; SVM; Color histogram;
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