Synthesized Performance Tuning towards Optimality in Identification and Tracking of Motion Detection for Video Surveillance System Using Image Processing Techniques

Abstract

The identification of motion tracking is a complex procedure due to its immense density in clarifying the object position over time with proper care on pixel variations.  The nest level of tracking the motion detection is a highly complicated procedure when the basic image processing approaches are only used.  The composition of soft computing domains along with the image processing techniques plays the vital role in effective identification and tracking of motion detection in video surveillance system.  The synthesized performance tuning approaches using image processing techniques are the desired way to obtain the optimal results in identification and tracking of motion detection in video surveillance system.  The existing surveillance video object motion tracking approach methods fails in the areas of deep analysis in tracking and predicting the movements in an optimal way since they entirely depends on basic image processing procedures.  The primary objective of this research is to focus on 3 main objectives such as optimal identification of motion detection, optimal tracking of motion detection and optimal prediction in identification, and tracking of motion detection along with the verification for optimality.  This research article proposes a synthesized performance tuning towards optimality in identification and tracking of motion detection for video surveillance system using image processing techniques.  In near future this research will be extended with the implementation of automated system using virtual reality for robotics.