Segmentation of Moving Objects using Numerous Background Subtraction Methods for Surveillance Applications
Supriya Agrawal1, Prachi Natu2
1Supriya Agrawal*, Department of Computer Engineering, NMIMS University, Mumbai, India.
2Dr. Prachi Natu, Department of Computer Engineering, NMIMS University, Mumbai, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2553-2563 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8811019320/2020©BEIESP | DOI: 10.35940/ijitee.C8811.019320
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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: Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.
Keywords: Background Subtraction, Video Surveillance, Foreground Detection, Object Detection, Illumination Changes, Occlusion, Human Activity Tracking, Background Modeling, Performance
Scope of the Article: Human Computer Interactions