Robust Human Body Tracking using PCA and SVM Classifier
Nirmala M1, Hamsaveni N2
1Nirmala M, Pursing her Master Degree in Digital Electronics, S. J. B. Institute of Technology, Bangalore, India.
2Hamsaveni N, Asso. Prof, in S. J. B. Institute of Technology, Bangalore, India.
Manuscript received on May 25, 2014. | Revised Manuscript received on June 19, 2014. | Manuscript published on June 30, 2014. | PP: 329-333 | Volume-3, Issue-5, June 2014. | Retrieval Number: E3253063514/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper deals with an intelligent image processing method for the video surveillance systems. We propose a technology detecting and tracking moving Human Body, which can be applied to consumer electronics such as home and business surveillance systems consisting of an internet protocol (IP) camera and a network video recorder (NVR). A real-time surveillance system needs to detect moving objects robustly against noises and environment. In the proposed system SVM classifies the data in a wide variety range of applications. SVM is powerful to approximate any training data and generalizes better on given datasets. Extended Kalman filter which makes the system more robust by tracking and reduce the noise introduced by inaccurate detections. Extended Kalman filter outperforms other state-of –the –art algorithms in terms of efficiency, robustness and accuracy.
Keywords: Multiple moving object tracking, (IP) camera and a network video recorder (NVR).