Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton

A new method called Matrix Descriptor of Changes (MDC) is introduced in this work for description and recognition of human activity from sequences of skeletons. The primary focus was on one of the main problems in this area which is different duration of activities; it is assumed that the beginning and the end are known. Some existing methods use bag of features, hidden Markov models, recurrent neural networks or straighten the time interval by different sampling so that each activity has the same number of frames to solve this problem. The essence of our method is creating one or more matrices with a constant size. The sizes of matrices depend on the vector dimension containing the per-frame low-level features from which the matrix is created. The matrices then characterize the activity, even if we assume that certain activities may have different durations. The principle of this method is tested with two types of input features: (i) 3D position of the skeleton joints and (ii) invariant angular features of the skeleton. All kinds of feature types are processed by MDC separately and, in the subsequent step, all the information gathered together as a feature vector are used for recognition by Support Vector Machine classifier. Experiments have shown that the results are similar to results of the state-of-the-art methods. The primary contribution of proposed method was creating a new simple descriptor for activity recognition with preservation of the state-of-the-art results. This method also has a potential for parallel implementation and execution.