Depth-based 3D hand pose tracking

In this paper, we propose two new approaches using the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) for tracking 3D hand poses. The first approach is a detection based algorithm while the second is a data driven method. Our first contribution is a new tracking-by-detection strategy extending the CNN based single frame detection method to a multiple frame tracking approach by taking into account prediction history using RNN. Our second contribution is the use of RNN to simulate the fitting of a 3D model to the input data. It helps to relax the need of a carefully designed fitting function and optimization algorithm. With such strategies, we show that our tracking frameworks can automatically correct the fail detection made in previous frames due to occlusions. Our proposed method is evaluated on two public hand datasets, i.e. NYU and ICVL, and compared against other recent hand tracking methods. Experimental results show that our approaches achieve the state-of-the-art accuracy and efficiency in the challenging problem of 3D hand pose estimation.