Comparative Study And Implementation Of Background Modeling Techniques For Background Subtraction

  • Mahasweta J. Joshi , Jitendra P. Chaudhari


The goal of human action recognition is to routinely examine ongoing activities from an unknown video. For a video sequences, action recognition perceive the supreme comparable action between the action arrangements found out by way of the system. In a modest case wherein a video is segmented to incorporate most effective one execution of a human activity, the objective of the action recognition system is to properly categorize the video into its appropriate activity class. Vision based Human Action Recognition (HAR) technique encompass three steps: Background subtraction, Feature Extraction and Classification. Recognizing not stable objects from a video is a major as well as basic task in numerous computer-vision applications. In non-movable camera segmentation, the device - camera is established in a specific angle and position. Here the background will always stable, it is easy and usual to construct a background model in advance, so that the foreground entity can be segmented. Dissimilar to non-movable camera, moving camera (for example a camera is fitted on cars, robots or flying vehicles, etc.), is used with active site and position. Moving camera segmentation is considerably more difficult than non-movable camera segmentation. In this paper, comparative study of Background generation methods for Background Subtraction Technique have been discussed and implemented using various literature available for Background Subtraction.

How to Cite
Mahasweta J. Joshi , Jitendra P. Chaudhari. (2020). Comparative Study And Implementation Of Background Modeling Techniques For Background Subtraction. International Journal of Advanced Science and Technology, 29(12s), 2955-2965. Retrieved from