Video activity analysis based on 3D wavelet statistical properties

A video activity analysis is presented based on 3D wavelet transform. Marginal and joint statistics as well as mutual information estimates are extracted. Marginal histograms are approximated by generalized Gaussian density (GGD) functions. The mutual information between coefficients -as a quantitative estimate of joint statistics- decreases when the activity in the video increases. The relationship between kurtosis graphs, extracted from joint distributions and video activity, is deduced. Results show that the type of activity in the video can be figured out from Kurtosis curves. The GGD and the Kullback-Leibler distance (KLD) are used to retrieve and locate 96% of videos properly.