東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computer Vision Algorithms for Mobil...

Ozcan, Koray.

FindBook

Google Book

Amazon

博客來

Computer Vision Algorithms for Mobile Camera Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computer Vision Algorithms for Mobile Camera Applications./
作者:	Ozcan, Koray.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	164 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-02, Section: B.
Contained By:	Dissertations Abstracts International79-02B.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10265202
ISBN:	9780355055078

Computer Vision Algorithms for Mobile Camera Applications.
Ozcan, Koray.

Computer Vision Algorithms for Mobile Camera Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 164 p.

Source: Dissertations Abstracts International, Volume: 79-02, Section: B.

Thesis (Ph.D.)--Syracuse University, 2017.

This item must not be sold to any third party vendors.

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices.

ISBN: 9780355055078Subjects--Topical Terms:

1567821
Computer Engineering.

Computer Vision Algorithms for Mobile Camera Applications.
LDR:04006nmm a2200337 4500 001 2207466
005 20190920131231.5
008 201008s2017 ||||||||||||||||| ||eng d
020 $a 9780355055078
035 $a (MiAaPQ)AAI10265202
035 $a (MiAaPQ)syr:11560
035 $a AAI10265202
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ozcan, Koray. $3 3434454
245 1 0 $a Computer Vision Algorithms for Mobile Camera Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 164 p.
500 $a Source: Dissertations Abstracts International, Volume: 79-02, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Velipasalar, Senem.
502 $a Thesis (Ph.D.)--Syracuse University, 2017.
506 $a This item must not be sold to any third party vendors.
520 $a Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices.
590 $a School code: 0659.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a Syracuse University. $b Electrical Engineering and Computer Science. $3 3169988
773 0 $t Dissertations Abstracts International $g 79-02B.
790 $a 0659
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10265202