A multi-balanced hybrid optimization technique to track objects using rough set theory. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Shanmugapriya, K.; Malar, R.
• Is Part Of: Signal, image and video processing. Volume 11:Issue 3(2017)
• Page Start: 415
• Page End: 421
• Publication Date: 2017-03
• Subjects: Rough set theory -- HGA-PSO -- Moving object -- Detection and tracking; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0976-4
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract In this paper, the process of object detection and tracking is performed by means of five stages, namely frame segregation, shot segmentation, shape and texture feature extraction, object detection in frames through rough set theory and soft computing evolutionary programming with hybrid genetic algorithm particle swarm optimization. In the first stage, the input video file is segregated into number of frames and then the image frame from the specific shots is alone separated in the second stage with the help of DCT transformations. The third phase involves extracting shape and texture features from the shot segmented image frames.