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Multimodal Person Detection System.
~
Southern Connecticut State University.
Multimodal Person Detection System.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Multimodal Person Detection System./
Author:
Barello, Philip M.
Description:
1 online resource (31 pages)
Notes:
Source: Masters Abstracts International, Volume: 57-01.
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9780355498318
Multimodal Person Detection System.
Barello, Philip M.
Multimodal Person Detection System.
- 1 online resource (31 pages)
Source: Masters Abstracts International, Volume: 57-01.
Thesis (M.S.)--Southern Connecticut State University, 2017.
Includes bibliographical references
Person detection is often critical for national security, personal safety, and property protection. Most person detection technology implements unimodal classification, making predictions based on a single sensor data modality, which is often vision. There are many ways to defeat unimodal person detectors, and many more reasons to ensure technologies responsible for detecting the presence of a person are accurate and precise. Our multimodal person detection system provides a framework to acquire, store, classify, and evaluate person detection predictions based on a variety of possible unimodal classifications and multimodal sensory data fusions. We find evidence that multimodal fusion of predictions based on different sensor modalities can generate predictions with higher accuracy, precision, and recall values, with lower false postive and false negative rates than unimodal classification from video only. This work provides examples of commodity sensors and software configured for multimodal person detection, aiming to make the concept, process, and benefits of improved multimodal person detection more accessible to others.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355498318Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Multimodal Person Detection System.
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Source: Masters Abstracts International, Volume: 57-01.
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Advisers: MD S. Hossain; Amal A. El-Raouf.
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Thesis (M.S.)--Southern Connecticut State University, 2017.
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Includes bibliographical references
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Person detection is often critical for national security, personal safety, and property protection. Most person detection technology implements unimodal classification, making predictions based on a single sensor data modality, which is often vision. There are many ways to defeat unimodal person detectors, and many more reasons to ensure technologies responsible for detecting the presence of a person are accurate and precise. Our multimodal person detection system provides a framework to acquire, store, classify, and evaluate person detection predictions based on a variety of possible unimodal classifications and multimodal sensory data fusions. We find evidence that multimodal fusion of predictions based on different sensor modalities can generate predictions with higher accuracy, precision, and recall values, with lower false postive and false negative rates than unimodal classification from video only. This work provides examples of commodity sensors and software configured for multimodal person detection, aiming to make the concept, process, and benefits of improved multimodal person detection more accessible to others.
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click for full text (PQDT)
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