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Acoustic and Image Vehicle Classific...

California State University, Long Beach.

Acoustic and Image Vehicle Classification System.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Acoustic and Image Vehicle Classification System./
作者:	Hoang, Trong Tuyen.
面頁冊數:	1 online resource (56 pages)
附註:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355230260

Acoustic and Image Vehicle Classification System.
Hoang, Trong Tuyen.

Acoustic and Image Vehicle Classification System. - 1 online resource (56 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)

Includes bibliographical references

The objective of this project is to detect and classify vehicles into two categories -- 1) light vehicles, such as cars and other passenger vehicles and 2) heavy vehicles: trucks, busses, and other commercial vehicles, using video, sound, as well as joint video and sound classifications.

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

Mode of access: World Wide Web

ISBN: 9780355230260Subjects--Topical Terms:

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

554714
Electronic books.

Acoustic and Image Vehicle Classification System.
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006 m o u
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008 190606s2017 xx obm 000 0 eng d
020 $a 9780355230260
035 $a (MiAaPQ)AAI10604463
035 $a (MiAaPQ)csulb:12021
035 $a AAI10604463
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Hoang, Trong Tuyen. $3 1183700
245 1 0 $a Acoustic and Image Vehicle Classification System.
264 0 $c 2017
300 $a 1 online resource (56 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: 56-06.
500 $a Adviser: I-Hung Khoo.
502 $a Thesis (M.S.) $c California State University, Long Beach $d 2017.
504 $a Includes bibliographical references
520 $a The objective of this project is to detect and classify vehicles into two categories -- 1) light vehicles, such as cars and other passenger vehicles and 2) heavy vehicles: trucks, busses, and other commercial vehicles, using video, sound, as well as joint video and sound classifications.
520 $a For the video portion, images from a video were analyzed. The physical size of a vehicle was extracted and stored. From this, the classification of different vehicles was made possible. In regards to the sound portion, the vehicles were recorded acoustically and their sound signature was analyzed. From these sound waves, it is desirable to extract not only the energy levels, wideband spectrogram, and converting it to the frequency domain, but to also represent it on a nonlinear mel scale of frequency. This has been frequently done for automatic speech recognition, but it is also feasible to extract the mel-frequency cepstral coefficients to use as features to aid the classification process of vehicles. Using this, one can train a machine learning algorithm - such as an artificial neural network - and have it label vehicles as one of the two classes.
520 $a Furthermore, the combination of both the video and the sound portion makes it possible to detect and classify vehicles more accurately than if using each system separately. This potentially allows for a more reliable system, especially when the image classification system by itself is not able to properly detect or classify vehicles, i.e. in heavy fog, or at night.
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 California State University, Long Beach. $b Electrical Engineering. $3 1180451
773 0 $t Masters Abstracts International $g 56-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10604463 $z click for full text (PQDT)

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