Real time big-data processing and monitoring tool for object detection using Gaussian Mixture model with improved noise reduction
Pranjal Pandey, Rakesh Prasad, Dilip Kumar Singh · Research Square · 2024
Abstract Increasing number of road accidents draws major concern over on-road safety. Road accidents are leading causes of deaths across the world. Numerous approaches have been made to improve the monitoring and control system to avoid road accidents like use of sensors, radar based onboard electronic devices etc, Machine learning based monitoring is one approach towards object detection to avoid accidents due to human error and system failure. In the present work we have reported about development of an improved monitoring tool to detect moving objects in a video frame in real time and with improved noise reduction using Gaussian Mixture Model. We have used machine learning model with the help of MATLAB’s computer vision tool which do not require an external digital data storage space. This results in development of a tool which is capable of detecting the moving object in a live video footage which is capable of differentiating between foreground and background in continuously varying daylight conditions and effective for noise reduction. The developed technique can be applied to selectively record the eventful frames from live video reducing the requirement of human intervention for monitoring and large data storage for record. In addition to surveillance for road transport, the tool can be used for the purpose of monitoring at hospitals, airports, military and defence purpose.