MULTIPLE-OBJECT MULTI-FEATURE FAST COMPRESSIVE TRACKING

International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 10, Issue 2, March-April 2019, pp. 896-914, Article ID: IJARET_10_02_089 Available online at https://iaeme.com/Home/issue/IJARET?Volume=10&Issue=2 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: https://doi.org/10.17605/OSF.IO/32XBP © IAEME Publication Scopus Indexed MULTIPLE-OBJECT MULTI-FEATURE FAST COMPRESSIVE TRACKING S. V. Suresh Babu Matla Research Scholar, Department of Computer Science, School of Engineering and Technology, Pondicherry University, India S. Ravi Associate Professor, Department of Computer Science, School of Engineering and Technology, Pondicherry University, India J. Vaishnavi Research Scholar, Department of Computer Science, School of Engineering and Technology, Pondicherry University, India A. Anbarasi Research Scholar, Department of Computer Science, School of Engineering and Technology, Pondicherry University, India Key words: Object detection, multiple object detection, kalman filtering, multiple-feature extraction sparse representation model, SVM Cite this Article: S. V. Suresh Babu Matla, S. Ravi, J. Vaishnavi and A. Anbarasi, Multiple-object multi-feature fast compressive tracking, International Journal of Advanced Research in Engineering and Technology (IJARET), 2019, 10(2), pp. 896-914. DOI: https://doi.org/10.17605/OSF.IO/32XBP https://iaeme.com/MasterAdmin/Journal_uploads/IJARET/VOLUME_10_ISSUE_2/IJARET_10_02_089.pd

PARTICLE SWARM OPTIMIZATION BASED FAST COMPRESSIVE TRACKER FOR DETECTING COMPLETE OCCLUSION

Abstract Object tracking has become a wide spread technology in both applications and scientific domains. Many object tracking algorithms has been proposed, but many challenging factors such as occlusion, pose variation, illumination change were predominant in those object trackers. In order to overcome these issues, a novel fast compressive tracking techniques is proposed, in which the object features are extracted and compressed to a sparse-random matrix, which preserves the originality of the image. Naïve Bayes Classier is employed for detecting and updating the extracted features, thereby reducing the computational complexity. But, the compressed tracking methodology failed to detect objects with missing frames and complete occlusion. In order to address this issue, a novel fast compressive tracking - particle swarm optimization (FCT-PSO) model is proposed, which will be able to detect objects from suddenly missing frames and with complete occlusion. A sparse random matrix is used to extract the features from the sample image to a compressed domain. Taking the extracted feature from the sample image, FCT-PSO methodology determines the next optimal frame position using the best frame position in the past, thus paving for object detection under complete occlusion and missed frames. Cite this Article: S. V. Suresh Babu Matla, S. Ravi, J. Vaishnavi and A. Anbarasi, Particle Swarm Optimization based Fast Compressive Tracker for Detecting Complete Occlusion, International Journal of Advanced Research in Engineering and Technology (IJARET), 2019, 10(6), pp. 755-77