ABSTRACT

The focus of this project is on development of a domestic environment that is cognizant of the whereabouts and activities of its occupants and can support them in their everyday life. While the technology is applicable to a range of domestic situations, the emphasis in this work will be on support for aging in place; through collaboration with experts in assistive care and cognitive aging, the PI and his team will design, demonstrate, and evaluate a series of domestic services that aim to maintain the quality of life for an aging population, with the goal of increasing the likelihood of a “stay at home” alternative to assisted living that satisfies the needs of an aging individual and his/her distributed family. In particular, the PI will explore two areas that are key to sustaining quality of life for an independent senior adult: maintaining familial vigilance, and supporting daily routines. The intention is to serve as an active partner, aiding the senior occupant without taking control. This research will lead to advances in three research areas: human-computer interaction; computational perception; and software engineering. To achieve the desired goals, the PI will conduct the research and experimentation in an authentic domestic setting, a novel research facility called the Residential Laboratory recently completed next to the Georgia Tech campus. Together with experts in theoretical and practical aspects of aging, the PI will establish a pattern of research in which informed design of ubiquitous computing technology can be rapidly deployed, evaluated and evolved in an authentic setting. Special attention will be paid throughout to issues relating to privacy and trust implications. The PI will transition the products of this project to researchers and practitioners interested in performing more large-scale observations of the social and economic impact of Aware Home technologies.

ABSTRACT

Stop motion animation is a well-established technique where still pictures of static scenes are taken and then played at film speeds to show motion. A major limitation of this method appears when fast motions are desired; most motion appears to have sharp edges and there is no visible motion blur. Appearance of motion blur is a strong perceptual cue, which is automatically present in live-action films, and synthetically generated in animated sequences. In this paper, we present an approach for automatically simulating motion blur. Ours is wholly a post-process, and uses image sequences, both stop motion or raw video, as input. First we track the frame-to-frame motion of the objects within the image plane. We then integrate the scene’s appearance as it changed over a period of time. This period of time corresponds to shutter speed in live-action filming, and gives us interactive control over the extent of the induced blur. We demonstrate a simple implementation of our approach as it applies to footage of different motions and to scenes of varying complexity. Our photorealistic renderings of these input sequences approximate the effect of capturing moving objects on film that is exposed for finite periods of time.

Abstract

We present a new technique that improves upon existing structure from motion (SFM) methods. We propose a SFM algorithm that is both recursive and optimal. Our method incorporates innovative information from new frames into an existing solution without optimizing every camera pose and scene structure parameter. To do this, we incrementally optimize larger subsets of parameters until the error is minimized. These additional parameters are included in the optimization by tracing connections between points and frames. In many cases, the complexity of adding a frame is much smaller than full bundle adjustment of all the parameters. Our algorithm is best described us incremental bundle adjustment as it allows new information to be added to art existing non-linear least-squares solution

April 5, 2001 Issue

Quote from the Article: “Cameras are going to rule one day at the Georgia Tech house, though, staff members there say. Dr. Irfan A. Essa, a computer science professor at Georgia Tech, is one of the people building a tracking system, based on video cameras, that will one day replace radio frequency tags. ”We can locate where the person is,” Dr. Essa said, ”and make a first-level guess at where this person is heading using the optical sensors.””