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http://dx.doi.org/10.6109/jkiice.2016.20.8.1537

Object Segmentation/Detection through learned Background Model and Segmented Object Tracking Method using Particle Filter  

Lim, Su-chang (Department of Computer Science, Sunchon National University)
Kim, Do-yeon (Department of Computer Engineering, Sunchon National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.8, 2016 , pp. 1537-1545 More about this Journal
Abstract
In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.
Keywords
Background Subtraction; Background Modeling; Object Segmentation; Object Tracking; Particle Filter;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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