Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detected motion classification with a double-background and a neighborhood-based difference
Pattern Recognition Letters
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Objects, Shadows and Ghosts in Video Streams by Exploiting Color and Motion Information
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Visible and infrared sensors fusion by matching feature points of foreground blobs
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Visible and infrared image registration using trajectories and composite foreground images
Image and Vision Computing
Computer Vision and Image Understanding
Iterative division and correlograms for detection and tracking of moving objects
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Image and Vision Computing
Computer Vision and Image Understanding
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In this paper, three dynamic background subtraction algorithms for colour images are presented and compared. The performances of these algorithms defined as 'Selective Update using Temporal Averaging', 'Selective Update using Non-foreground Pixels of the Input Image' and 'Selective Update using Temporal Median' are only different for background pixels. Then using an invariant colour filter and a suitable motion tracking technique, an object-level classification is offered that recognises the behaviours of all foreground blobs. This novel approach, which selectively excludes foreground blobs from the background frames, is included in all three methods. It is shown that the 'Selective Update using Temporal Median' produces the correct background image for each input frame. The advantages of the third algorithm are: it operates in unconstrained outdoor and indoor scenes. Also it is able to handle difficult situations such as removing ghosts and including stationary objects in the background image efficiently. Meanwhile, the algorithm's parameters are computed automatically or are fixed. The efficiency of the new algorithm is confirmed by the results obtained on a number of image sequences.