DeepAI
Log In Sign Up

An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance

07/22/2013
by   Subra Mukherjee, et al.
0

Efficient security management has become an important parameter in todays world. As the problem is growing, there is an urgent need for the introduction of advanced technology and equipment to improve the state-of art of surveillance. In this paper we propose a model for real time background subtraction using AGMM. The proposed model is robust and adaptable to dynamic background, fast illumination changes, repetitive motion. Also we have incorporated a method for detecting shadows using the Horpresert color model. The proposed model can be employed for monitoring areas where movement or entry is highly restricted. So on detection of any unexpected events in the scene an alarm can be triggered and hence we can achieve real time surveillance even in the absence of constant human monitoring.

READ FULL TEXT
06/22/2015

Target Tracking In Real Time Surveillance Cameras and Videos

Security concerns has been kept on increasing, so it is important for ev...
09/13/2019

Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding

Video rain/snow removal from surveillance videos is an important task in...
03/03/2018

Real-Time Deep Learning Method for Abandoned Luggage Detection in Video

Recent terrorist attacks in major cities around the world have brought m...
10/09/2018

Real time expert system for detecting object region and working state of aerators

Aerators are essential and important auxiliary devices in intensive cult...
02/02/2015

Towards a solid solution of real-time fire and flame detection

Although the object detection and recognition has received growing atten...
02/01/2018

Automatic Safety Helmet Wearing Detection

Surveillance is essential for the safety of power substation. The detect...
09/30/2011

A Novel comprehensive method for real time Video Motion Detection Surveillance

This article describes a comprehensive system for surveillance and monit...