Biorthogonal wavelet transform based classification of human object using Adaboost classifier

Human object classification in video is an important problem in computer vision. The main challenge is to correctly classify any human object in presence of several objects, occlusion of object, varying background and lighting conditions, etc. Object classification is much desired in surveillance like applications. Several classification algorithms have been proposed in spatial and wavelet domain. In this paper we present an object classification application of machine learning which is based on the single feature of object. The feature chosen for classification is energy of biorthogonal wavelet transform (BWT) coefficients of the object and is used to classify the object in a video into two categories: human object and non-human object. Two important properties of biorthogonal wavelet transform - shift invariance and symmetry, are useful for object classification. Translation in object is well handled by the shift invariance property while symmetry property is used to maintain the object boundaries. Classification has been performed using Adaboost classifier. Quantitative analysis of the results demonstrates better performance of the proposed method over other state-of-the-art methods.