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Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video dataset exists for benchmarking different methods. Presented here is a unique change detection benchmark dataset consisting of nearly(More)
In this paper, we propose an efficient, robust, and fast method for the estimation of global motion from image sequences. The method is generic in that it can accommodate various global motion models, from a simple translation to an eight-parameter perspective model. The algorithm is hierarchical and consists of three stages. In the first stage, a low-pass(More)
This paper presents a new approach to the estimation of 2-D motion vector fields from time-varying images. The approach is stochastic both in its formulation and in the solution method. The formulation involves the specification of a deterministic structural model along ivith stochastic observation and motion field models. Two motion models are proposed: a(More)
Despite a significant growth in the last few years, the availability of 3D content is still dwarfed by that of its 2D counterpart. To close this gap, many 2D-to-3D image and video conversion methods have been proposed. Methods involving human operators have been most successful but also time-consuming and costly. Automatic methods, which typically make use(More)
Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated(More)
Automatic recognition of human actions from video has been studied for many years. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. However, the best-performing methods are complex and computationally-demanding and thus(More)
Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The most successful background subtraction methods apply probabilistic models to background intensities evolving in time; nonparametric and mixture-of-Gaussians models are but two examples. The main difficulty in designing a robust(More)
The Kinect has primarily been used as a gesture-driven device for motion-based controls. To date, Kinect-based research has predominantly focused on improving tracking and gesture recognition across a wide base of users. In this paper, we propose to use the Kinect for biometrics; rather than accommodating a wide range of users we exploit each user's(More)