Video Surveillance for Effective Object Detection with Alarm Triggering

Abstract

This paper presents a novel algorithm for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and under- goes both gradual and sudden once-off changes with fixed background and static cameras. Our method achieves complete detection of moving objects by involving three significant proposed modules: a background modeling (BM) module, an alarm trigger (AT) module, and an object extraction (OE) module For our proposed BM module, a unique two- phase background matching procedure is performed using rapid matching followed by accurate matching in order to produce optimum background pixels for the background model. Next, our proposed AT module eliminates the unnecessary examination of the entire background region, allowing the subsequent OE module to only process blocks containing moving objects. And the alarm triggers while a new object enters into the frame, the foreground information and background information are identified using the reference frame as background model . Here , both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance. Index Terms: Background modeling, background subtraction, video segmentation, video surveillance, Alarm Triggering ,Shadow Removal.