Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems
Raviprakash M L1, C S Pillai2, Ananda babu J3

1Mr. Raviprakash M L, Assistant Professor in Department of Computer Science and Engineering, Kalpataru Institute of Technology, Tiptur.
2Dr. C S Pillai, Associate Professor in Department of Computer Science and Engineering, ACS College of Engineering, Banglore, India.
3Dr. Ananda babu J, Associate Professor in Department of Computer Science and Engineering, Malnad College of Engineering, Hassan, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2219-2224 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9712109119/2019©BEIESP | DOI: 10.35940/ijeat.A9712.109119
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.
Keywords: Object recognition , video surveillance systems, Shi-Tomasi approach, Lucas-Kanade techniques, MATLAB.