SIAM Journal on Scientific and Statistical Computing
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications
Neural Networks
Background Modeling for Segmentation of Video-Rate Stereo Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
IEEE Transactions on Intelligent Transportation Systems
Multimedia Tools and Applications
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Background modeling via incremental maximum margin criterion
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Robust moving object detection against fast illumination change
Computer Vision and Image Understanding
Computers & Mathematics with Applications
Background subtraction based on phase feature and distance transform
Pattern Recognition Letters
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
On line background modeling for moving object segmentation in dynamic scenes
Multimedia Tools and Applications
A novel background subtraction method based on color invariants
Computer Vision and Image Understanding
Fast background subtraction using static and dynamic gates
Artificial Intelligence Review
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In video surveillance, detection of moving objects from an image sequence is very important for target tracking, activity recognition, and behavior understanding. Background subtraction is a very popular approach for foreground segmentation in a still scene image. In order to compensate for illumination changes, a background model updating process is generally adopted, and leads to extra computation time. In this paper, we propose a fast background subtraction scheme using independent component analysis (ICA) and, particularly, aims at indoor surveillance for possible applications in home-care and health-care monitoring, where moving and motionless persons must be reliably detected. The proposed method is as computationally fast as the simple image difference method, and yet is highly tolerable to changes in room lighting. The proposed background subtraction scheme involves two stages, one for training and the other for detection. In the training stage, an ICA model that directly measures the statistical independency based on the estimations of joint and marginal probability density functions from relative frequency distributions is first proposed. The proposed ICA model can well separate two highly-correlated images. In the detection stage, the trained de-mixing vector is used to separate the foreground in a scene image with respect to the reference background image. Two sets of indoor examples that involve switching on/off room lights and opening/closing a door are demonstrated in the experiments. The performance of the proposed ICA model for background subtraction is also compared with that of the well-known FastICA algorithm.