《Tab.4 Average recognition accuracy by using different feature extraction methods》

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《Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning》

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In order to verify the validity of the application of NS-DTCWPT and Cayley-Klein metric learning to group activity recognition，the proposed algorithm is compared with the algorithm of DTCWT combined with Mahalanobis metric.Recognition results on different video sets are shown in Fig.8-Fig.1 3，where the color boxes of red，blue，rose red，green，yellow，cyan and white represent the activities of fighting，walk together，dancing，talking，jogging，crossing and waiting respectively.The last images in Figs.8-1 3respectively show the recognition results when Gauss white noises are added to behave video set，group activity video set and self-built video set.Because NS-DTCWPT is more effective than DTCWT for representing geometrical features such as edges and textures，and Cayley-Klein metric learning is more suitable than Mahalanobis for modeling and measuring data with complex geometric structure，the proposed algorithm has better recognition results than the algorithm of DTC-WT combined with Mahalanobis metric，as seen from Figs.8-1 3.Recognition results shown on the last noise images in Figs.8-1 3demonstrate the advantage of the complete translation invariance of NS-DTCWPT.Figs.1 4-1 5give quantitative evaluation results of different algorithms by using confusion matrix.Non-diagonal row element of the confusion matrix represents the probability that certain type of group activity is recognized as other type of group activity，and the diagonal element represents the probability of correct recognition.Average recognition accuracy by using different feature extraction methods is shown in Tab.4.From Figs.1 4-1 5and Tab.4，it is verified that the proposed algorithm has better recognition accuracy.