Abstract In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance
Computer Vision – ACCV 2006 ; 4 ; 328-337
Lecture Notes in Computer Science ; 3851 , 4
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Background Model , Visual Tracking , Foreground Object , Foreground Pixel , Video Segmentation Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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