Action recognition with various speeds and timed-DMHI feature vectors

Usually, various motion recognition approaches can not perform well on actions that have variable speeds, or part of the dataset is speedy and vice versa. In this paper, we present the timing issue of our Directional Motion History Image method. This method can solve overwriting or motion self-occlusion problem significantly and thereby it performs well for complex and repetitive activities. However, it is important to analyze the method with activities having various speeds to show its robustness. The experimental results demonstrate that it can perform well with variable paces of the actions though the recognition rate is compromised a bit compared to the dataset having usual speed. We also improve the classification method with the incorporation of motion duration in the final feature vector so that for similar type of activities having different pace, it can show better result. Experiments on another dataset show better performance with the timing incorporation. The achieved recognition rates are very encouraging for further research and implementation is some intelligent systems for surveillance and related applications.