This paper introduces a novel motion capturing framework which works by minimizing the fitting error between an ellipsoid based skeleton and the input point cloud data captured by multiple depth cameras. The novelty of this method comes from that it uses the ellipsoids equipped with the spherical harmonics encoded displacement and normal functions to capture the geometry details of the tracked object. This method is also integrated with a mechanism to avoid collisions of bones during the motion capturing process. The method is implemented parallelly with CUDA on GPU and has a fast running speed without dedicated code optimization. The errors of the proposed method on the data from Berkeley Multimodal Human Action Database (MHAD) are within a reasonable range compared with the ground truth results. Our experiment shows that this method succeeds on many challenging motions which are failed to be reported by Microsoft Kinect SDK and not tested by existing works. In the comparison with the state-of-art marker-less depth camera based motion tracking work our method shows advantages in both robustness and input data modality.

Sensor deployment is a fundamental issue in a wireless sensor network, which often dictates the overall network performance. Previous studies on sensor deployment mainly focused on sensor networks on 2D plane or in 3D volume. In this paper, we tackle the problem of optimal sensor deployment on 3D surfaces, aiming to achieve the highest overall sensing quality. In general, the reading of a sensor node exhibits unreliability, which often depends on the distance between the sensor and the target to be sensed, as observed in a wide range of applications. Therefore, with a given set of sensors, a sensor network offers different accuracy in data acquisition when the sensors are deployed in different ways in the Field of Interest (FoI). We formulate this optimal surface deployment problem in terms of sensing quality by introducing a general function to measure the unreliability of monitored data in the entire sensor network. We present its optimal solution and propose a series of algorithms for practical implementation. Extensive simulations are conducted on various 3D mountain surface models to demonstrate the effectiveness of the proposed algorithms.