MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641073574 A) filed by Prince Shri Venkateshwara Padmavathy Engineering College; Prince Dr K Vasudevan College Of Engineering And; Prince Shri Balaji Arts And Science College; and Prince Shri Venkateshwara Arts And Science College on June 13, 2026, for Optimizing And Background Learning In A Single Process Of Moving Object Detection.
Inventors include Prince Shri Venkateshwara Padmavathy Engineering; Prince Dr K Vasudevan College Of Engineering And; Prince Shri Balaji Arts And Science College; and Prince Shri Venkateshwara Arts And Science College.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: Video surveillance systems have long been in use to monitor security sensitive areas. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. Moving object detection is the basic step for further analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes such as human, vehicle, animal, clutter, etc. It is necessary to distinguish objects from each other in order to track and analyse their actions reliably. In existing system performed background subtraction by using Canny Edge Detection. In Canny Edge Detection process we are taking two images for comparison those are background image and foreground image. Previous strategies for immense, including object detectors (supervised learning), image segmentation, Background subtraction, etc... Our methodology aims to segment objects supported motion info and it comprises an element of background modelling. In the existing system are conducting background subtraction only for images. For this we proposed a pixel wise background modelling and subtraction technique using multiple features. Hence in this colour, gradient and Hear like features are integrated to handle the variation pixel.
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