Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

• Published in: IEEE transactions on image processing Vol. 18; no. 1; pp. 158 - 167
• Main Authors: TSAI, Du-Ming, LAI, Shia-Chih
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Background subtraction; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; foreground segmentation; Frequency estimation; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image recognition; Image segmentation; Image sequences; Independent component analysis; independent component analysis (ICA); indoor surveillance; Information, signal and communications theory; Layout; Object detection; Pattern recognition; Pattern Recognition, Automated - methods; Principal Component Analysis; Reproducibility of Results; Security Measures; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal, noise; Studies; Subtraction Technique; Target recognition; Target tracking; Telecommunications and information theory; Video surveillance; Moving target; Segmentation; Updating; Activity levels; Remote sensing; foreground segmentation; Learning; Indoor installation; Image sequence; independent component analysis (ICA); Probability density function; Fixed image; Target detection; Monitoring; Multistage method; Target tracking; Background; Background subtraction; Independent component analysis; Gesture recognition; Video technique; Pattern recognition; Object recognition; Image method; Statistical method; Computation time; indoor surveillance; Object detection; Foreground; Remote supervision
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.2007558

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.