THRESHOLD BASED FILTERING TECHNIQUE FOR EFFICIENT MOVING OBJECT DETECTION AND TRACKING IN VIDEO SURVEILLANCE

Abstract

Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has been developed for video object detection, however the object detection accuracy and background object detection in the video frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function, particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe-art works.