Foreground Moving Object Detection using Support Vector Machine (SVM)
M. Nagaraju1, M. Baburao2
1M. Nagaraju*, Research scholar, CSE, JNTUK, Kakinada, India.
2Second Author M. Baburao, Professor, CSE, Gudlavalleru Engineering College, Gudlavalleru, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2354-2356 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8265118419/2019©BEIESP | DOI: 10.35940/ijrte.D8265.118419
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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: Detect the existence of an object and to locate it in a video is called object detection. In the process of tracking an object we first segment the given frame and then track its position, motion and occurrence. Before tracking any object the process of object detection and object classification are done. Once we detect any object we have to classify the object into several categories like humans, vehicles, animals etc. There are many applications in which object tracking is being used such as robotics, traffic monitoring, security, video surveillance and animations. In this paper, we proposed a framework to detect the foreground moving object in a video scene at real time. Firstly preprocessing the given video is divided into frames to detect the object. Next morphological filter is used to remove the Nosie after detect the object. In this step, frame is divided into pixels. Here optical flow method is used to segment the given frames. After that, canny edge detector is used to detect the edge of object from segmented image. Finally, classify the object using Support Vector Machine (SVM).
Keywords: SVM, IQA, Gaussian Filter, Morphology.
Scope of the Article: Machine Design.