RADIOENGINEERING, VOL. 25, NO. 2, JUNE 2016 399 DOI: 10.13164/re.2016.0399 SYSTEMS Segmentation of Moving Objects using Background Subtraction Method in Complex Environments Satrughan KUMAR, Jigyendra Sen YADAV Department of Electronics and Communication, MANIT, Bhopal (462003), India satrughankumar@gmail.com, jsyadav74@gmail.com Manuscript received June 26, 2015 Abstract. Background subtraction is an extensively used approach to localize the moving object in a video se- quence. However, detecting an object under the spatiotem- poral behavior of background such as rippling of water, moving curtain and illumination change or low resolution is not a straightforward task. To deal with the above-men- tioned problem, we address a background maintenance scheme based on the updating of background pixels by estimating the current spatial variance along the temporal line. The work is focused to immune the variation of local motion in the background. Finally, the most suitable label assignment to the motion field is estimated and optimized by using iterated conditional mode (ICM) under a Mar- kovian frame- work. Performance evaluation and compari- sons with the other well-known background subtraction methods show that the proposed method is unaffected by the problem of aperture distortion, ghost image, and high frequency noise. Keywords Background subtraction, background modeling, initial motion field, morphology 1. Introduction Moving object segmentation in video frames is the most significant step in many computer vision applications including human activity analysis, traffic monitoring, and video surveillance [1]. However, the complexities to iden- tify suspicious activities of people at social places and endangered object at shopping interacts, airports, banks, have become a matter of concern and motivated others toward the development of precise and robust surveillance systems [2]. In short, motion detection is a way to determine the magnitude of point or group of points in two or more con- secutive images of a video sequence, which are non-sta- tionary. In compulsion, object segmentation and motion perception in a video frames are a prerequisite of many post-processing steps such as target classification, behavior recognition, monitoring [3], [4]. Some of the existing methods for motion detection are optical flow [5], frame difference [6], statistical method [7] and background sub- traction [8–11]. The frame difference method is robust and has a strong adaptability in varying environment along with less computation time and complexity. However, it creates holes inside the target due to incomplete generation of relevant pixel on the fore-ground mask. Optical flow is a reliable approach for local motion speculation, but it de- mands hardware in real time putting into use. On the other hand, background subtraction is a simplistic way to local- ize the target in a scene without the any prior information about the scene. Although, the background subtraction method is inexpensive with respect to memory requirement and computational time, yet it faces a few difficulties to contend with accuracy under spatiotemporally behavior of the background object. Traditional background subtraction schemes such as AMF (Approximated Median Filter) [12], Kalman filter [13], and single Gaussian filter [14] reflect some irrelevant pixels on the foreground due to lack of correlation between the spatial and temporal constraints in their background maintenance schemes. Nevertheless, adjusting the learning rate to background pixels is another potential problem in background maintenance [15]. The adaptive algorithms based on fast learning rate quickly absorb the environ- mental noise and contravene the generations of entire rele- vant pixels of the target. However, the algorithms based on low learning rate are less robust against a slow moving object and show the ways to generate multiple images or ghost on the foreground image [16]. Furthermore, these algorithms do not integrate any data validation techniques that exploit the inter-pixel relationship to reduce the miss- classification on the foreground mask. In this paper, we focus to enhance the robustness of the background subtraction method under static and dy- namic conditions of background [17]. Initially, the spatial and temporal constraints are mapped to exclude the impul- sive effect of the registered background model. A region level processing is conducted to assign the proper label to the moving object on the foreground image. The rest of the paper is organized as follows: Section 2 presents the es- sential related work concerning background modeling and