Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

SkyDOT (Sky Database for Objects in the Time Domain): A Virtual Observatory for Variability Studies at LANL

The mining of Virtual Observatories (VOs) is becoming a powerful new method for discovery in astronomy. Here we report on the development of SkyDOT (Sky Database for Objects in the Time domain), a new Virtual Observatory, which is dedicated to the study of sky variability. The site will confederate a number of massive variability surveys and enable exploration of the time domain in astronomy. We discuss the architecture of the database and the functionality of the user interface. An important aspect of SkyDOT is that it is continuously updated in near real time so that users can access new observations in a timely manner. The site will also utilize high level machine learning tools that will allow sophisticated mining of the archive. Another key feature is the real time data stream provided by RAPTOR (RAPid Telescopes for Optical Response), a new sky monitoring experiment under construction at Los Alamos National Laboratory (LANL).Comment: to appear in SPIE proceedings vol. 4846, 11 pages, 5 figure

Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans

In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments