Recognition of Human Activities in Daubechies Complex Wavelet Domain

Abstract

Recognition of accurate human activities is a challenging research problem in video surveillance problem of computer vision research. The task of recognizing activities of human from video sequence exhibits more challenges because of real time processing of data. In this paper, we have proposed a method for recognition of human activities based on Daubechies complex wavelet transform (DCxWT). Better edge representation and approximate shift invariant properties of DCxWT over the other real valued wavelet transform motivates us to utilize properties of DCxWT in recognition of human activities. The multi-class SVM is used for classifying the recognized human activities. The proposed method is compared with other state-of-the-art method, on various standard publicly available dataset, in terms of different quantitative performance measures. We found that the proposed method has better recognition accuracy in comparison to other state-of-the-art methods.