Abandoned Object Detection with Region of Interest
Divya C. Patil1, Pravin S. Patil2
1Ms. Divya C. Patil, Scholar, Department of Electronics Engineering, S.S.V.P.S’s B.S.D. College of Engineering, Dhule (Maharashtra)-424005, India.
2Dr. Pravin S. Patil, Professor, Department of Electronics Engineering, S.S.V.P.S’s B.S.D. College of Engineering, Dhule (Maharashtra)-424005, India
Manuscript received on January 01, 2017. | Revised Manuscript received on January 04, 2017. | Manuscript published on January 30, 2017. | PP: 22-27 | Volume-6 Issue-6, January 2017. | Retrieval Number: F2943016617/2017©BEIESP
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© The Authors. Published By: 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: Abandoned object detection is an essential requirement in many video surveillance contexts. In this paper, we propose a method to detect abandoned object from surveillance video. Different from conventional approaches that mostly rely on pixellevel processing, we perform region-level analysis in both background maintenance and static foreground object detection. In background maintenance, region-level information is fed back to adaptively control the learning rate. In static foreground object detection, region-level analysis double-checks the validity of candidate abandoned blobs. Different from conventional approaches that mostly rely on pixel-level processing, we perform region-level analysis. In this paper, we present an abandoned object detection system based on blob detection methods are aimed at detecting regions. In a digital image that differs in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant. All the points in a blob can be considered in some sense to be similar to each other. In this paper we are performing a real time application using the Raspberry Pi processor and a Raspberry Pi camera.
Keywords: Abandoned object, Video surveillance, Framing, Image, Pixels.