Hand Tracking Using Kernel Density Approximation

Due to its wide range of applications, the impact of multimedia in the real world has shown stupendous growth. Texts, images, audio, and video are the different forms of multimedia which are utilized by humans in various applications such as education and surveillance applications. A wide range of research has been carried out, and here in this paper, we propose an object racking with the aid of rough set theory in combination with the eminent soft computing technique evolutionary programming. Initially, the input video is segregated into frames, then the frames that belong to particular shots are identified through the shot segmentation process, and after that the object to be tracked is identified manually. Subsequently, the shape and texture feature is extracted, and then the rough set theory is applied. This is done to identify the presence of object in the frames. Consequently, genetic algorithm (GA) is utilized for the object monitoring process to mark the object with variant color. As a result, the selected object is tracked in an effective manner.

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Hand Tracking Using Kernel Density Approximation

Cited by 2 publications

References 34 publications

A multi-balanced hybrid optimization technique to track objects using rough set theory

A multi-balanced hybrid optimization technique to track objects using rough set theory

An Effective Technique to Track Objects with the Aid of Rough Set Theory and Evolutionary Programming

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