Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Neural Network Approach to Background Modeling for Video Object Segmentation
IEEE Transactions on Neural Networks
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Conventional background subtraction techniques that update a background model online have difficulties with correctly segmenting foreground objects if sudden brightness changes occur. Other methods that learn a global scene model offline suffer from projection errors. To overcome these problems, we present a different approach that is localand discriminative, i.e. for each pixel a classifier is trained to decide whether the pixel belongs to the background or foreground. Such a model requires significantly less tuning effort and shows a better robustness, as we will demonstrate in quantitative experiments on self-created and standard benchmarks. Finally, segmentation is improved significantly by integrating the probabilistic evidence provided by the local classifiers with a graph cut segmentation algorithm.