People Counting Using Wavelet Transform And Neural Network

With the development of the society, more and more people appeared in publicplaces and facilities. So, how to manage the crowd is a problem that we have to payspecial attention to. And intelligent crowd surveillance based on image processingemerges as requested. It can be applied to the fields of crowd surveillance andmanagement, market research, traffic safety, and building design. It may improvework efficiency of the above situations and the rate in use of buildings directly orindirectly. Research on people counting has deep meanings and wide future. The emphasis of the thesis is people counting using technology of image/videoprocessing and pattern recognition. And how to extract the features of head effectively,and how to classify people and background are the key technologies of this thesis. The thesis first introduces the structure and development of intelligent crowdsurveillance system. And then the limitations of present system are addressed. Thisthesis proposes a method of recognizing the individual in the crowd by detecting thehead, and then estimating the number of crowd. After the first and second grade of 2DHaar wavelet transform, we analyses the wavelet coefficients, and useback-propagation network to testify those coefficients selected as features. At last weselect the coefficients of HL sub band and LH sub band as features. The basis ofconcept in selecting those features is that the coefficients reveal the contour and thetexture of horizontal and vertical orientation. In order to reduce the rate of errordetection, Bootstrapping is used to enlarge the number of samples. And as follows askin model of YCbCr color space is taken as post-validation. Unlike the conventionalskin model, a model of adding lighting compensation that is presented newly is used.Finally, the head windows of same person using object clustering are merged. And thetest has got good results.