Research Article
Human Object Classification in Daubechies Complex Wavelet Domain
@INPROCEEDINGS{10.1007/978-3-319-05939-6_13, author={Manish Khare and Rajneesh Srivastava and Ashish Khare and Nguyen Binh and Tran Dien}, title={Human Object Classification in Daubechies Complex Wavelet Domain}, proceedings={Context-Aware Systems and Applications. Second International Conference, ICCASA 2013, Phu Quoc Island, Vietnam, November 25-26, 2013, Revised Selected Papers}, proceedings_a={ICCASA}, year={2014}, month={6}, keywords={Object classification Feature selection Daubechies complex wavelet transform (DCxWT) Adaboost classifier}, doi={10.1007/978-3-319-05939-6_13} }- Manish Khare
Rajneesh Srivastava
Ashish Khare
Nguyen Binh
Tran Dien
Year: 2014
Human Object Classification in Daubechies Complex Wavelet Domain
ICCASA
Springer
DOI: 10.1007/978-3-319-05939-6_13
Abstract
Human object classification is an important problem for smart video surveillance applications. In this paper we have proposed a method for human object classification, which classify the objects into two classes: human and non-human. The proposed method uses Daubechies complex wavelet transform coefficients as a feature of object. Daubechies complex wavelet transform is used due to its better edge representation and approximate shift-invariant property as compared to real valued wavelet transform. We have used Adaboost as a classifier for classification of objects. The proposed method has been tested on standard datasets like, INRIA dataset. Quantitative experimental evaluation results show that the proposed method is better than other state-of-the-art methods and gives better performance.
