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dc.contributor.authorPatra, D-
dc.contributor.authorKumar K, S-
dc.contributor.authorChakraborty, D-
dc.identifier.citationIEEE India Council Conference, INDICON 2009; Ahmedabad; 18 December 2009 through 20 December 2009; Category number CFP09598; Code 79708; Article number 5409361en
dc.description.abstractIn this paper we propose a novel method for object tracking in video images. The method is based on image segmentation and pattern matching. All moving and still objects in video images can be detected accurately with the help of efficient image segmentation techniques. We propose a hybrid algorithm for image segmentation using the notion of Particle Swarm Optimization (PSO) and Fuzzy-C-Means (FCM) clustering techniques. The results obtained using segmentation of successive frames are exploited for pattern matching in a simple feature space. As a consequence, multiple moving and still objects in video images are tracked simultaneously. We perform simulation experiments on object tracking to validate the efficiency of our proposed algorithm. The algorithm outperforms the existing algorithm in context of accuracy and time complexity.en
dc.format.extent179628 bytes-
dc.subjectFuzzy-C-Means clustering;en
dc.subjectImage segmentation;en
dc.subjectMotion estimation;en
dc.subjectObject tracking;en
dc.subjectParticle Swarm optimization;en
dc.subjectPattern matchingen
dc.titleObject Tracking in Video Images Using Hybrid Segmentation Method and Pattern Matchingen
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