Massimo Camplani

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Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely chan ging the comp uter vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation(More)
Low-cost depth cameras, such as Microsoft Kinect, have completely changed the world of human-computer interaction through controller-free gaming applications. Depth data provided by the Kinect sensor presents several noise-related problems that have to be tackled to improve the accuracy of the depth data, thus obtaining more reliable game control platforms(More)
This work addresses the challenge of analysing the quality of human movements from visual information which has use in a broad range of applications, from diagnosis and rehabilitation to movement optimisation in sports science. Traditionally, such assessment is performed as a binary classification between normal and abnormal by comparison against normal and(More)
Efficient Spatio-Temporal Hole Filling Strategy for Kinect Depth Authors Camplani,, Massimo; Salgado, Luis Citation Abstract In this paper we present an efficient hole filling strategy that improves the quality of the depth maps obtained with the Microsoft Kinect device. The proposed approach is based on a joint-bilateral filtering framework that includes(More)
There's a widely known need to revise current forms of healthcare provision. Of particular interest are sensing systems in the home, which have been central to several studies. This article presents an overview of this rapidly growing body of work, as well as the implications for machine learning, with an aim of uncovering the gap between the state of the(More)
In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and(More)
An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by(More)
We present a real-time RGB-D object tracker which manages occlusions and scale changes in a wide variety of scenarios. Its accuracy matches, and in many cases outperforms, state-of-the-art algorithms for precision and it far exceeds most in speed. We build our algorithm on the existing colour-only KCF tracker which uses the ‘kernel trick’ to extend(More)