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, multibackgroundcongestion, 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 time-frequency 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.