Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spatially distributed model for foreground segmentation
Image and Vision Computing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
A PCA-based technique to detect moving objects
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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Real time segmentation of scene into objects and background is really important and represents an initial step of object tracking. Starting from the codebook method [4] we propose some modifications which show significant improvements in most of the normal and also difficult conditions. We include parameter of frequency for accessing, deleting, matching and adding codewords in codebook or to move cache codewords into codebook. We also propose an evaluation method in order to objectively compare several segmentation techniques, based on receiver operating characteristic (ROC) analysis and on precision and recall method. We propose to summarize the quality factor of a method by a single value based on a weighted Euclidean distance or on a harmonic mean between two related characteristics.