International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) pp. 1866-1873 © Research India Publications. http://www.ripublication.com 1866 A Novel Method to Improve Basic Background Subtraction Methods for Object Detection in Video Surveillance System Surender Singh Research Scholar, School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India. Orcid Id: 0000-0002-7996-8588 Ajay Prasad Associate Professor, School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India. Kingshuk Srivastava Assistant Professor, School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India. Suman Bhattacharya Project Head, IPR Management Services, Tata Consultancy Services, Bhubneswar, India. Abstract This paper proposes a novel method for the improvement of basic Background Subtraction (BGS) methods to detect moving objects in video surveillance streams. The method is based on Local Neighborhood Differencing (LND) in which instead of finding a simple pixel to pixel difference between current frame and background model, the average of the pixel neighborhoods from the current frame and background model are subtracted to entitle the pixel a background or foreground in the current frame in order to find moving objects in video. The proposed method has been tested on two basic methods; Adaptive Mean and Adaptive Median methods of object detection using various complex real time benchmarked scenarios. It is also compared with classical statistical thresholding method. The results have been measured in precision and recall metrics to register improvement. The obtained results have confirmed the utility of the method by increasing the robustness of the object detection techniques in video surveillance for real time video analytic. Keywords: Object Detection, Motion Detection, Background Subtraction, Automated Video Surveillance, Adaptive Mean, Adaptive Median, P-R Curves. INTRODUCTION Real-time video surveillance systems detect objects or situations in video flow that represent a security threat and trigger an alarm accordingly. These systems can be classified into operator controlled, automated video surveillance and intelligent video surveillance systems [19]. In operator controlled surveillance system, the video stream is analyzed manually; a person observes the video to determine if there is any activity that requires an action. In the second approach, the automated video surveillance system uses motion detection techniques to determine response. An intelligent video surveillance system is that which extract the relevant information from generic motion accurately and issue actions. Existing video surveillance systems take care about video capture, store and transmission of video to remote places but devoid of efficient object detection and analysis leaving these functions exclusively to human operators for manual analysis [6]. Therefore, there is an urgent need of a surveillance system which is fast, efficient and accurate. There are several categories of object detection methods out of which the background subtraction is most popular and traditionally used category [18]. In this category, there are robust methods such as Kernel Density Estimation and Histogram Detection which provide reliable detection but these are also slow and less useful for real time analytics. This category also includes some basic methods like Frame Differencing, Adaptive Mean, Adaptive Median methods which are fast but do not provide good object detection results. Our research is targeted to improve these basic BGS methods for object detection. This paper is organized as follows: Section 1 identifies the background on the need of modification in object detection paradigm to make it more robust and useful for real time scenarios. Section 2 presents the related works which made efforts to improve the basic methods. Section 3 describes the proposed methodology. Section 4 tabulates and compares the results obtained with the proposed methods and other past improvements in basic methods. Section 5 discusses the results which is followed by conclusion and future scope in section 6. RELATED WORKS Most of the improvements made in the past in basic background subtraction methods revolves around proposing a threshold which is effective and adaptive in different situations or scenarios [1]. Many statistical measures such as mean, median, deviation, outliers and variance from mean are used to define a