Machine Vision and Applications DOI 10.1007/s00138-012-0475-8 ORIGINAL PAPER Change detection for moving object segmentation with robust background construction under Wronskian framework Badri Narayan Subudhi · Susmita Ghosh · Ashish Ghosh Received: 24 September 2011 / Revised: 18 September 2012 / Accepted: 3 December 2012 © Springer-Verlag Berlin Heidelberg 2013 Abstract Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static back- ground, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed back- ground model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wron- skian framework. The Wronskian based change detection model is further used to detect the changes between the con- structed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spa- tial region of support in this regard. The proposed scheme B. N. Subudhi · A. Ghosh (B ) Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India e-mail: ash@isical.ac.in B. N. Subudhi e-mail: subudhi.badri@gmail.com S. Ghosh Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India e-mail: susmitaghoshju@gmail.com relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spa- tial and temporal modes. The results obtained by the pro- posed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques. Keywords Background subtraction · Object detection · Temporal analysis · Gaussian mixture models 1 Introduction An important problem in computer vision is the detection of moving objects from a video sequence [7]. Proper detec- tion of moving objects is crucial for many computer vision and artificial intelligent systems. Moving object detection has been widely applied in the fields like visual surveillance [23, 27], face and gait-based human recognition [29, 37], activity recognition [17], robotics [28], etc. Background subtraction (BGS) method [22] is very pop- ular for video object detection. It needs to handle different situations like illumination variation, noise, non-static back- ground, and shadow of the moving objects in the scene. The effects of noise and illumination variation are very com- mon in daily life video scenes [4]. In this regard a robust BGS scheme using running Gaussian average was proposed by Wren et al. [36]. Here, the authors have modeled the background independently at each pixel location by fitting a Gaussian probability distribution function (pdf) over a sequence of previous/past image frames. The parameters of the Gaussian pdf (i.e., the mean and the variance) at each 123