Video Classification by Main Frequencies of Repeating Movements

This paper discusses an approach, which allows classifying videos by frequencies. Many videos contain repetitive activities like walking, swimming, or playing table tennis. These activities usually repeat with a certain frequency, which can be seen as a feature of cyclic motion. So determining this feature can help to classify videos with repeating movements properly. In this paper we explain how to find out the right frequencies for video clips and how to use them for classifying. The main method handles series of image moments as a function in order to transform this function into the frequency domain via FFT. Techniques proposed in this paper are tested with own and with external video data. Thus it can be pointed out how the system handles different data types and data qualities.