Mobile computing and Mobile Commerce is most popular now a days because of the service offered during the mobility. Mobile computing has become the reality today rather than the luxury.Mobile wireless market is increasing by leaps and bounds. The quality and speeds available in the mobile environment must match the fixed networks if the convergence of the mobile wireless and fixed communication network is to happen in the real sense. The challenge for mobile network lies in providing very large footprint of mobile services with high speed and security. Online transactions using mobile devices must ensure high security for user credentials and it should not be possible for misuse. M-Commerce is the electronic commerce performed using mobile devices. Since user credentials to be kept secret, a high level of security should be ensured.
In this research work, we propose a method for human action recognition based on the combination of structural and temporal features. The pose sequence in the video is considered to identify the action type. The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds. The displacement vector of joint locations is used to compute the temporal features. The structural variation features and the temporal variation features are fused using a neural network to perform action classification. We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets. The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.
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