Background Subtraction Based on Color and Depth Using Active Sensors

Depth information has been used in computer vision for a wide variety of tasks. Since active range sensors are currently available at low cost, high-quality depth maps can be used as relevant input for many applications. Background subtraction and video segmentation algorithms can be improved by fusing depth and color inputs, which are complementary and allow one to solve many classic color segmentation issues. In this paper, we describe one fusion method to combine color and depth based on an advanced color-based algorithm. This technique has been evaluated by means of a complete dataset recorded with Microsoft Kinect, which enables comparison with the original method. The proposed method outperforms the others in almost every test, showing more robustness to illumination changes, shadows, reflections and camouflage.This work was supported by the projects of excellence from Junta de Andalucia MULTIVISION (TIC-3873), ITREBA (TIC-5060) and VITVIR (P11-TIC-8120), the national project, ARC-VISION (TEC2010-15396), and the EU Project, TOMSY (FP7-270436)

Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

Improving the sensitivity of future GW observatories in the 1-10 Hz band: Newtonian and seismic noise

The next generation gravitational wave interferometric detectors will likely be underground detectors to extend the GW detection frequency band to frequencies below the Newtonian noise limit. Newtonian noise originates from the continuous motion of the Earth’s crust driven by human activity, tidal stresses and seismic motion, and from mass density fluctuations in the atmosphere. It is calculated that on Earth’s surface, on a typical day, it will exceed the expected GW signals at frequencies below 10 Hz. The noise will decrease underground by an unknown amount. It is important to investigate and to quantify this expected reduction and its effect on the sensitivity of future detectors, to plan for further improvement strategies. We report about some of these aspects. Analytical models can be used in the simplest scenarios to get a better qualitative and semi-quantitative understanding. As more complete modeling can be done numerically, we will discuss also some results obtained with a finite-element-based modeling tool. The method is verified by comparing its results with the results of analytic calculations for surface detectors. A key point about noise models is their initial parameters and conditions, which require detailed information about seismic motion in a real scenario. We will describe an effort to characterize the seismic activity at the Homestake mine which is currently in progress. This activity is specifically aimed to provide informations and to explore the site as a possible candidate for an underground observatory. Although the only compelling reason to put the interferometer underground is to reduce the Newtonian noise, we expect that the more stable underground environment will have a more general positive impact on the sensitivity.We will end this report with some considerations about seismic and suspension noise