Sketch on dynamic gesture tracking and analysis exploiting visionbased 3D interface In this paper, we propose a visionbased 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 sensorbased motion tracking systems. Visionbased 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 Labanu0027s Effort theory and movement analysis by mapping features to meaningful symbols using timedelay 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 boxbased 3D interface lessens the difficulties in both tracking movement in 3D space and in extracting lowlevel features of the movement. Furthermore, the timedelay 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 Icubed 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.

RealTime 3D Hand Gesture Based Mobile Interaction Interface Hand gesture recognition is a challenging problem for natural humancomputer interaction(HCI). We address this problem by introducing a realtime humanmobile interaction interface with a depth sensor. Our interface consists of two components, 3D hand pose estimation and hand skeleton state based gesture description. Firstly, we propose a 3D hand pose estimation method that combines learning based pose initialization and physical based model fitting, which can estimate the perframeu0027s hand pose that appears in the depth camerau0027s field of view. Afterwards, we map the estimated pose to gesture, e.g. open or close, through a hand skeleton state based method. With the tracked hand gesture, we can stably and smoothly implement common operations such as u0027Touchu0027, u0027Graspu0027 and u0027Holdu0027 with midair interface. Our main contribution is combine 3D hand pose estimation and hand gesture tracking, and implementing an interaction application system with the details.