Abstract. We propose a new approach to the correspondence problem that makes use of non-parametric local transforms as the basis for correlation. Non-parametric local transforms rely on the relative o r d e ring of local intensity v alues, and not on the intensity v alues themselves. Correlation using such transforms can tolerate a signi cant n umber of outliers. This can result in improved performance near object boundaries when compared with conventional methods such as normalized correlation. We i n troduce two non-parametric local transforms: the rank transform, which measures local intensity, and the census transform, w h i c h summarizes local image structure. We describe some properties of these transforms, and demonstrate their utility o n b o t h s y n thetic and real data.

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400).

We present an approach to real-time person tracking in crowded and/or unknown environments using multi-modal integration. We combine stereo, color, and face detection modules into a single robust system, and show an initial application in an interactive, face-responsive display. Dense, real-time stereo processing is used to isolate users from other objects and people in the background. Skin-hue classification identifies and tracks likely body parts within the silhouette of a user. Face pattern detection discriminates and localizes the face within the identified body parts. Faces and bodies of users are tracked over several temporal scales: short-term (user stays within the field of view), medium-term (user exits/reenters within minutes), and long term (user returns after hours or days). Short-term tracking is performed using simple region position and size correspondences, while medium and long-term tracking are based on statistics of user appearance. We discuss the failure modes of each individual module, describe our integration method, and report results with the complete system in trials with thousands of users.

Segmentation of novel or dynamic objects in a scene, often referred to as "background subtraction" or "joreground segmentation", is a critical early in step in most computer vision applications in domains such as surveillance and human-computer interaction. All previously described, real-time methods fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-rejections, similarity of foreground color to background, and non-static backgrounds (e.g. active video displays or trees waving in the wind). The recent advent of hardware and software for real-time computation of depth imagery makes better approaches possible. We propose a method for modeling the background that uses per-pixel, time-adaptive, Gaussian mixtures in the combined input space of depth and luminance-invariant colol: This combination in itself is novel, but we further improve it by introducing the ideas of I) modulating the background model learning rate based on scene activity, and 2 ) making colorbased segmentation criteria dependent on depth observations. Our experiments show that the method possesses much greater robustness to problematic phenomena than the prior state-of-the-art, without sacrijicing real-time performance, making it well-suited for a wide range of practical applications in video event detection and recognition.

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