Sketch on dynamic gesture tracking and analysis exploiting vision-based 3D interface

In this paper, we propose a vision-based 3D interface exploiting invisible 3D boxes, arranged in the personal space (i.e. reachable space by the body without traveling), which allows robust yet simple dynamic gesture tracking and analysis, without exploiting complicated sensor-based motion tracking systems. Vision-based gesture tracking and analysis is still a challenging problem, even though we have witnessed rapid advances in computer vision over the last few decades. The proposed framework consists of three main parts, i.e. (i) object segmentation without bluescreen and 3D box initialization with depth information, (ii) movement tracking by observing how the body passes through the 3D boxes in the personal space and (iii) movement feature extraction based on Laban's Effort theory and movement analysis by mapping features to meaningful symbols using time-delay neural networks. Obviously, exploiting depth information using multiview images improves the performance of gesture analysis by reducing the errors introduced by simple 2D interfaces. In addition, the proposed box-based 3D interface lessens the difficulties in both tracking movement in 3D space and in extracting low-level features of the movement. Furthermore, the time-delay neural networks lessens the difficulties in movement analysis by training. Due to its simplicity and robustness, the framework will provide interactive systems, such as ATR I-cubed Tangible Music System or ATR Interactive Dance system, with improved quality of the 3D interface. The proposed simple framework also can be extended to other applications requiring dynamic gesture tracking and analysis on the fly.