Information assimilation and dissemination is the major task and challenge in the new era of technology. There are many means of information storing. Underwater Videos and images reveal much information when they are analyzed. The proposed method uses Mixture of Gaussian as a basic model to segment the moving object under dynamic condition. The proposed method remove the motion of the underwater algae or plants which exists as background by checking the status of each foreground pixels in each frame and decides whether to be present as output or not in the post processing stage. Finally the output is compared with a validated ground truth.
Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.
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