Andrew C. Blose

Family portals: connecting families through a multifamily media space

Video conferencing allows distance-separated family members to interact somewhat akin to being together at the same place and time. Yet most video conferencing systems are designed for phone-like calls between only two locations. Using such systems for long interactions or social gatherings with multiple families is cumbersome, if not impossible. For this reason, we wanted to explore how families would make use of a video system that permitted sharing everyday life over extended periods of time between multiple locations. We designed a media space called Family Portals that provides shared video between three locations and deployed it within the homes of six families. Results show that the media space increased feelings of connectedness and the focus on a triad, in contrast to a dyad, caused new styles of interaction to emerge. Despite this, families experienced new privacy challenges and non-adoption by some family members, not previously seen in dyadic family media spaces.

Jointly estimating demographics and height with a calibrated camera

One important problem in computer vision is to provide a demographic description a person from an image. In practice, many of the state-of-the-art methods use only an analysis of the face to estimate the age and gender of a person of interest. We present a model that combines two problems, height estimation and demographic classification, which allows each to serve as context for the other. Our idea is to use a calibrated camera for measuring the height of people in the scene. Height is measured by jointly inferring across anthropometric dimensions, age, and gender using publicly available statistics. The height estimate provides context for recognizing the age and gender of the subject, and likewise age and gender conditions the distribution of the anthropometric features for estimating height. The performance of our method is explored on a new database of 127 people captured with a calibrated camera with recorded height, age, and gender. We show that estimating height leads to improvements in age and gender classification, and vice versa. To the best of our knowledge, our model produces the most accurate automatic height estimates reported, with the error having a standard deviation of 26.7 mm.

Automated videography for residential communications

The current widespread use of webcams for personal video communication over the Internet suggests that opportunities exist to develop video communications systems optimized for domestic use. We discuss both prior and existing technologies, and the results of user studies that indicate potential needs and expectations for people relative to personal video communications. In particular, users anticipate an easily used, high image quality video system, which enables multitasking communications during the course of real-world activities and provides appropriate privacy controls. To address these needs, we propose a potential approach premised on automated capture of user activity. We then describe a method that adapts cinematography principles, with a dual-camera videography system, to automatically control image capture relative to user activity, using semantic or activity-based cues to determine user position and motion. In particular, we discuss an approach to automatically manage shot framing, shot selection, and shot transitions, with respect to one or more local users engaged in real-time, unscripted events, while transmitting the resulting video to a remote viewer. The goal is to tightly frame subjects (to provide more detail), while minimizing subject loss and repeated abrupt shot framing changes in the images as perceived by a remote viewer. We also discuss some aspects of the system and related technologies that we have experimented with thus far. In summary, the method enables users to participate in interactive video-mediated communications while engaged in other activities.

An automated workflow for observing track data in 3-dimensional geo-accurate environments

Recent developments in computing capabilities and persistent surveillance systems have enabled advanced analytics and visualization of image data. Using our existing capabilities, this work focuses on developing a unified approach to address the task of visualizing track data in 3-dimensional environments. Our current structure from motion (SfM) workflow is reviewed to highlight our point cloud generation methodology, which offers the option to use available sensor telemetry to improve performance. To this point, an algorithm outline for navigation-guided feature matching and geo-rectification in the absence of ground control points (GCPs) is included in our discussion. We then provide a brief overview of our onboard processing suite, which includes real-time mosaic generation, image stabilization, and feature tracking. Exploitation of geometry refinements, inherent to the SfM workflow, is then discussed in the context of projecting track data into the point cloud environment for advanced visualization. Results using the new Exelis airborne collection system, Corvus Eye, are provided to discuss conclusions and areas for future work.