Tracking Animated Object in Video via Image Content Modeling
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Abstract
Tracking and recognizing the target using a film or a sequence of consecutive images is one of the most significant fields of research in machine vision. In the current research, a spatial-temporal content model is used to track images in the video. In this method, at first, a Spatio-temporal content model between the target object and the surrounding spatial background is learned based on the spatial relationships of a scene. The next time stage is performed using an assurance mapping design in tracking that can integrate Spatio-temporal content information and estimate the location of the target object by maximizing the assurance mapping. Likewise, content is used to help track moving objects in complex scenes and to try to reduce side effects and background interference. The simulation results are offered for fixed and moving camera modes, and lastly, the range of parameters designed to track the moving object in both modes is expressed. The simulation results reveal the speed, power, and accuracy of the suggested algorithm.
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