javed_wmvc_2002 - A Hierarchical Approach to Robust...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information Omar Javed, Khurram Shafique and Mubarak Shah Computer Vision Lab, School of Electrical Engineering and Computer Science, University of Central Florida E-mail: { ojaved,khurram,shah } @cs.ucf.edu Abstract We present a background subtraction method that uses multiple cues to robustly detect objects in adverse conditions. The algorithm consists of three distinct levels i.e pixel level, region level and frame level. At the pixel level, statistical models of gradients and color are separately used to classify each pixel as belonging to background or foreground. In re- gion level, foreground pixels obtained from the color based subtraction are grouped into regions and gradient based sub- traction is then used to make inferences about the validity of these regions. Pixel based models are updated based on de- cisions made at the region level. Finally frame level analy- sis is performed to detect global illumination changes. Our method provides the solution to some of the common prob- lems that are not addressed by most background subtraction algorithms such as quick illumination changes, repositioning of static background objects, and initialization of background model with moving objects present in the scene. 1. Introduction All Automated surveillance systems require some mech- anism to detect interesting objects in the field of view of the sensor. Such a mechanism serves as a form of focus of at- tention. Once objects are detected, the further processing for tracking and activity is limited in the corresponding regions of the image. In vision based systems, such detection is usu- ally carried out by using background subtraction methods. These methods build a model of the scene background, and for each pixel in the image, detect deviations of pixel fea- ture values from the model to classify the pixel as belonging either to background or to foreground. This pixel based in- formation is then grouped to make a similar classification of regions in the image. Though, pixel intensity or color are the most commonly used features for scene modelling, recently some effort has been made to combine this information with edges [7]. The Background differencing methods have to deal with several problems in realistic environments. These problems have been discussed in detail by Toyama et.al [15]. Here we briefly describe some of the important problems which have not been addressed by most background subtraction al- gorithms. Quick illumination changes : Quick illumination changes completely alter the color characteristics of the background, and thus increase the deviation of background pixels from the background model in color or intensity based subtraction. This results in a drastic increase in the number of falsely detected foreground regions and in the worst case, the whole image appears as foreground. This shortcoming makes surveillance under partially cloudy days almost impossible.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 6

javed_wmvc_2002 - A Hierarchical Approach to Robust...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online