Foreground detection using texture-based codebook method for monitoring systems

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Monitoring systems have been extensively applied in daily life as a result of technological advancements. Foreground detection is an important technique to refine monitoring systems. Dynamic backgrounds in the environment form a rather challenging issue in foreground detection. For example, fluttering leaves, wavelets on water, flashes on monitor screens, shadow changes are all likely to lead to erroneous analysis in foreground detection. This study proposes a foreground detection algorithm, called texture-based codebook method, which using a codebook model in combination with texture features. The codebook model used to construct a background model can effectively compress data to achieve optimal processing speeds, whereas the local binary pattern is adopted to identify texture features because of its powerful capacity for texture description and fast computation. Subsequently, texture-based connected component labeling is applied to the results from the codebook model to increase accuracy and reduce error rates. In addition, to adapt to the environment, a short-term information model has been added for better background model updates. Gradient-based time difference is also incorporated to cope with global light changes. The experiment outcome shows that the algorithm proposed is capable of real-time processing and also has good adaptability. It is able to achieve decent recognition rates when tested in different environments.