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Motivation: Recognizing human actions in a video is a challenging task whichhas applications in various fields. Previous works in this area have eitherused images from a 2D or 3D camera. Few have used the idea that human actionscan be easily identified by the movement of the joints in the 3D space andinstead used a Recurrent Neural Network (RNN) for modeling. Convolutionalneural networks (CNN) have the ability to recognise even the complex patternsin data which makes it suitable for detecting human actions. Thus, we modeled aCNN which can predict the human activity using the joint data. Furthermore,using the joint data representation has the benefit of lower dimensionalitythan image or video representations. This makes our model simpler and fasterthan the RNN models. In this study, we have developed a six layer convolutionalnetwork, which reduces each input feature vector of the form 15x1961x4 to anone dimensional binary vector which gives us the predicted activity. Results:Our model is able to recognise an activity correctly upto 87% accuracy. Jointdata is taken from the Cornell Activity Datasets which have day to dayactivities like talking, relaxing, eating, cooking etc.

Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data