Improved Adaptive Background Subtraction Method Using Pixel-based Segmenter

Moving object detection is essential in many computer vision systems as it is generally first process which feeds following algorithmic steps after getting camera stream. Thus quality of moving object detection is crucial for success of the whole process flow. It has been studied in the literature over the last two decades but it is still challenging issue because of factors such as background complexity, illumination variations, noise, occlusion and run-time performance requirement considering rapidly increasing image size and quality. In this paper, we try to contribute to solve this problem by improving an existing realtime non-parametric moving object detection method. In scope of this study, pixel based background model in which each pixel is represented separately by its distribution on time domain is used. Mentioned discrete background model is suitable for parallel processing by dividing the image to sub images in order to accelerate the process. Main feature of proposed nonparametric approach is automatic adjustment of algorithm parameters according to changes on the scene. This feature provides easy adaptation to environmental change and robustness for different scenes with unique parameter initialization. Another contribution is scene change detector to handle sudden illumination changes and adopt the background model to new scene in the fastest way. Experiments on 2012 ChangeDetection.net dataset show that our approach outperforms most state-of-the-art methods. Improvement obtained both on robustness and practical performance provides our approach to be able to use in real world monitoring systems.