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Multimodal Person Detection System.

Southern Connecticut State University.

Multimodal Person Detection System.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multimodal Person Detection System./
作者:	Barello, Philip M.
面頁冊數:	1 online resource (31 pages)
附註:	Source: Masters Abstracts International, Volume: 57-01.
標題:	Computer science. -
電子資源:	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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500 $a Source: Masters Abstracts International, Volume: 57-01.
500 $a Advisers: MD S. Hossain; Amal A. El-Raouf.
502 $a Thesis (M.S.)--Southern Connecticut State University, 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information technology. $3 559429
650 4 $a Statistics. $3 556824
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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