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Pedestrian Detection Based on Deep L...

Chen, Li.

Pedestrian Detection Based on Deep Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Pedestrian Detection Based on Deep Learning./
Author:	Chen, Li.
Description:	1 online resource (55 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-04.
Contained By:	Masters Abstracts International57-04(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355472189

Pedestrian Detection Based on Deep Learning.
Chen, Li.

Pedestrian Detection Based on Deep Learning. - 1 online resource (55 pages)

Source: Masters Abstracts International, Volume: 57-04.

Thesis (M.S.)--University of California, Riverside, 2017.

Includes bibliographical references

In general, researchers use hand-crafted methods or combine with the deep learning to solve the problem of Pedestrian Detection. In this paper, this problem can be implemented in the purely convolution neural network. Region Proposal Network, proposed by the algorithm for objects detection could be modified and applied on the pedestrian detection. After getting feature maps from the pretrained model, feed them into the new model and train by using Tensorflow as the deep learning framework, we can get the predicted bounding boxes that contain the pedestrians. This method is efficient and can reach the accuracy around 80 percent.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355472189Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Pedestrian Detection Based on Deep Learning.
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100 1 $a Chen, Li. $3 882495
245 1 0 $a Pedestrian Detection Based on Deep Learning.
264 0 $c 2017
300 $a 1 online resource (55 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-04.
500 $a Adviser: Qi Zhu.
502 $a Thesis (M.S.)--University of California, Riverside, 2017.
504 $a Includes bibliographical references
520 $a In general, researchers use hand-crafted methods or combine with the deep learning to solve the problem of Pedestrian Detection. In this paper, this problem can be implemented in the purely convolution neural network. Region Proposal Network, proposed by the algorithm for objects detection could be modified and applied on the pedestrian detection. After getting feature maps from the pretrained model, feed them into the new model and train by using Tensorflow as the deep learning framework, we can get the predicted bounding boxes that contain the pedestrians. This method is efficient and can reach the accuracy around 80 percent.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Riverside. $b Electrical Engineering. $3 845380
773 0 $t Masters Abstracts International $g 57-04(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622123 $z click for full text (PQDT)

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