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Video object tracking and segmentation are the fundamental building blocks for smart surveillance
system. Various algorithms like partial least square analysis, Markov model, Temporal differencing,
background subtraction algorithm, adaptive background updating have been proposed but each were having
drawbacks like object tracking problem, multibackground congestion, illumination changes, occlusion etc.
The background segmentation worked on to principled object tracking by using two models Gaussian mixture
model and level centre model. Wavelet transforms have been one of the important signal processing
developments, especially for the applications such as timefrequency analysis, data compression,
segmentation and vision. The key idea of the wavelet transform approach is to represents any arbitrary
function f (t) as a superposition of a set of such wavelets or basis functions. Results show that algorithm
performs well to remove occlusion and multibackground congestion as well as algorithm worked with
removal of noise in the signals
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