Background Subtraction of an Indian Classical Dance Videos using Adaptive Temporal Averaging Method
A slew of motion detection methods have been proposed in recent years. The background includes some constraints such as changes in illumination, shadow, cluttered the background, scene change and speed of dance between hand gestures and body gestures are different. One of the most basic methods for background subtraction is temporal averaging. We looked at a new adaptive thresholding approach in this paper. To identify moving objects in video sequences, an adaptive thresholding is used. Depending upon the speed of the technique we proposed a Gaussian distribution technique. Gaussian distribution done background subtraction depending upon active pixels it differentiates whether it is a background or foreground. The background model’s update rate has been modified to be adaptive and determined by pixel difference. Our aim is to improve the method’s F-measure by making it more adaptable to various scene scenarios. The experiment results are shown and evaluated. The proposed method and the original method’s quality parameters are compared.
