| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | September 12, 2013 |
| Latest Amendment Date: | April 29, 2015 |
| Award Number: | 1317788 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2013 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $292,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
FY 2015 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
2221 UNIVERSITY AVE SE STE 100 MINNEAPOLIS MN US 55414-3074 (612)624-5599 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
200 Union Street SE Minneapolis MN US 55455-0159 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Robust Intelligence, NRI-National Robotics Initiati |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
With advances in camera technologies, and as cloud storage, network bandwidth and protocols become available, visual media are becoming ubiquitous. Video recording became de facto universal means of instruction for a wide range of applications such as physical exercise, technology, assembly, or cooking. This project addresses the scientific and technological challenges of video shooting in terms of coverage and optimal views planning while leaving high level aspects including creativity to the video editing and post-production stages.
Camera placement and novel view selection challenges are modeled as optimization problems that minimize the uncertainty in the location of actors and objects, maximize coverage and effective appearance resolution, and optimize object detection for the sake of semantic annotation of the scene. New probabilistic models capture long range correlations when the trajectories of actors are only partially observable. Quality of potential novel views is modeled in terms of resolution that is optimized by maximizing the coverage of a 3D orientation histogram while an active view selection process for object detection minimizes a dynamic programming objective function capturing the loss due to classification error as well as the resources spent for each view.
The project advances active sensing and perception and provides the technology for further automation on video capturing. Such technology has broader impact on the production of education videos for online courses as well as in telepresence applications. Research results are integrated into robotics and digital media programs addressing K-12 students.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Many computer vision tasks such as scene coverage and object detection rely on the acquisition of high quality images related to the task. In this project, we explored mechanisms to actively control cameras to ensure the capture of such images. The algorithms can be used at a variety of scales ranging from planning the trajectories of a camera mounted on an aerial vehicle inspecting a field to the case of a camera mounted on a robot arm for visual inspection.
In terms of intellectual merit, the primary contributions of the project included an algorithm with theoretical performance guaranteess to visit a given set of viewing cones in the shortest amount of time. This problem arises in a number of settings including visual inspection of farms where the cones correspond to the viewing regions of areas of interest (e.g. potential infestations). We also developed a novel visual servoing platform where a camera is mounted on a robot arm and is controlled by interpreting the images acquired in an online fashion. We demonstrated the algorithm in an orchard inspection task which is challenging because the scene is not static (due to wind) and light conditions can be varying drastically: there can be bright, direct sunlight or many shadows.
In terms of broader impact, such view planning algorithms can be useful in a variety of applications of societal importance. These include agriculture as mentioned above, industrial inspection or search and rescue. Graduate and undergraduate students (some of whom were underrepresented minorities) were trained as part of this project.
Last Modified: 11/30/2017
Modified by: Ibrahim V Isler
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