Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm
Autor: | Saadia Medouakh, Nadjiba Terki, Mohamed Boumehraz |
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Rok vydání: | 2017 |
Předmět: |
Color histogram
business.industry Color normalization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Histogram matching 020206 networking & telecommunications Pattern recognition 02 engineering and technology Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Histogram Video tracking Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Mean-shift Artificial intelligence Electrical and Electronic Engineering business Histogram equalization ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
Zdroj: | Signal, Image and Video Processing. 12:583-590 |
ISSN: | 1863-1711 1863-1703 |
Popis: | In this paper, a new robust mean shift tracker is proposed by utilizing the joint color and texture histogram. The contribution of our work is to take local phase quantization (LPQ) operator advantage of texture features representation, and to combine it with a color histogram mean shift tracking algorithm. The LPQ technique can be applied to obtain the texture features which represent the object. In texture classification, The LPQ operator is much robust to blur than the well-known local binary pattern operator (LBP). Compared with traditional color histogram mean shift algorithm which considers only color statistical information of the object, the joint color-LPQ texture histogram is more robust and overcome some difficulties of the traditional color histogram mean shift algorithm. Comparative experimental results on numerous challenging image sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods, especially traditional mean shift tracker. The algorithm is evaluated by numerical parameters: the center location and the average overlap, it proved the tracking robustness in presence of similar target appearance and background, motion blurring. |
Databáze: | OpenAIRE |
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