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Various Aspects of a Structured Motion Database for Efficient Human Motion Recognition

Human motion recognition is an emerging research field for the video-based applications capable of recognizing human motions or actions. The automaticity of such a system has vital importance for implementation in practical scenarios. With the growth of enormous bulk motion dataset, it becomes an indispensable task to develop a motion database that can deal with large variability of motions efficiently. We have developed such a system based on the structured motion database concept. In order to gain a perspective on this issue, we have analyzed various aspects of the motion database with a view to establishing a standard recognition scheme. The conventional structured database is subjected to improvement by considering three aspects: directional organization, nearest neighbor searching problem resolution, and prior direction estimation. In order to investigate and analyze comprehensively the effect of those aspects on motion recognition, we have adopted two forms of motion representation, eigenspace-based motion compression, and B-Tree structured database. Performance evaluation is performed by synthesized 3D human motions observed from eight camera directions. The experimental results illustrate the suitability and compatibility of various aspects of the motion database.