Essential Body-Joint and Atomic Action Detection for Human Activity Recognition Using Longest Common Subsequence Algorithm

We present an effective algorithm to detect essential body-joints and their corresponding atomic actions from a series of human activity data for efficient human activity recognition/classification. Our human activity data is captured by a RGB-D camera, i.e. Kinect, where human skeletons are detected and provided by the Kinect SDK. Unique in our approach is the novel encoding that can effectively convert skeleton data into a symbolic sequence representation which allows us to detect the essential atomic actions of different human activities through longest common subsequence extraction. Our experimental results show that, through atomic action detection, we can recognize human activity that consists of complicated actions. In addition, since our approach is "simple", our human activity recognition algorithm can be performed in real-time.

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