Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image Editing in the Contour Domain
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
A binary wavelet decomposition of binary images
IEEE Transactions on Image Processing
Car detection in sequences of images of urban environments using mixture of deformable part models
Pattern Recognition Letters
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A novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. Since this approach is quite general, the model can approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame are transformed in the binary discrete wavelet domain, and background subtraction is performed in each sub-band. After obtaining the foreground, shadow is eliminated by an edge detection method. Experiments show that the simple method produces good results with much lower computational complexity and can effectively extract the moving objects, even though the objects are similar to the background, and shadows can be successfully eliminated, thus good moving objects segmentation can be obtained.