Selective Subtraction for Handheld Cameras

Background Subtraction for Freely Moving Cameras

Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. In this paper, we extend the concept of ‘subtracting’ areas at rest to apply to video captured from a freely moving camera. We do not assume that the background i...

Background Subtraction with PTZ Cameras

Diminished Reality using Multiple Handheld Cameras

In this paper, we present a system for Diminished Reality with multiple handheld camera system. In this paper, we assume a situation such that the same scene is captured with multiple handheld cameras, but some objects occlude the scene. In such case, we propose a method for synthesizing image for each camera in which the occluding objects are diminished by the support of the different cameras ...

Handheld scanning with ToF sensors and cameras

Novel real-time depth acquisition devices, like Time-OfFlight (ToF) cameras or Microsoft Kinect, allow a very fast and simple acquisition of 3D views. These sensors have been used in many applications but their employment for 3D scanning purposes is a very challenging task due to the limited accuracy and reliability of their data. In this paper we present a 3D acquisition pipeline explicitly ta...

A Multi-transformational Model for Background Subtraction with Moving Cameras

We introduce a new approach to perform background subtraction in moving camera scenarios. Unlike previous treatments of the problem, we do not restrict the camera motion or the scene geometry. The proposed approach relies on Bayesian selection of the transformation that best describes the geometric relation between consecutive frames. Based on the selected transformation, we propagate a set of ...