An improved Mean Shift tracking algorithm based on color and texture feature

Xiang-Zhang,; Dai, Yuxing; Chen, Zhangwei; Zhang, Huaixiang

doi:10.1109/icwapr.2010.5576453

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…The most common reason is that features can act to encode ad-hoc domain knowledge that is difficult to learn using a finite quantity of training data. Target object is tracked in successive using features of the target object in first frame [13][14][15][16][17][18]. In this approach color features and texture features are used for object tracking.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Object tracking using joint enhanced color-texture histogram

Diwakar

Patel

Gupta

et al. 2013

2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)

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Object tracking is an important task within the field of computer vision. There are two key steps in video analysis: detection of interesting moving objects and tracking of such objects from frame to frame. Mean shift object tracking algorithm is a feature based algorithm.In this paper color information of object are extracted as well as texture information is also extracted by using local binary pattern technique to represent the object. In joint color-texture histogram region of interest is extracted and then edges and corners are extracted from the region of interest. Color histogram is insensitive to nondeformation and partial occlusion but it does not perform well when in case of color aberration. To overcome this drawback Local Binary Pattern method is used. This feature makes this algorithm more efficient in comparison of color histogram algorithm.

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