國立高雄大學圖資館 |

Language: English

Back

Pedestrian Detection Using Deep Lear...

Texas A&M University - Kingsville.

Pedestrian Detection Using Deep Learning Through A Dashcam.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Pedestrian Detection Using Deep Learning Through A Dashcam.
Author:	Trivedi, Harshil Pareshkumar.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2019
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 81-04.
Notes:	Advisor: Goyal, Ayush;Hicks, David.
Contained By:	Masters Abstracts International81-04.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885475
ISBN:	9781085773799

Pedestrian Detection Using Deep Learning Through A Dashcam.
Trivedi, Harshil Pareshkumar.

Pedestrian Detection Using Deep Learning Through A Dashcam. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 47 p.

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.S.)--Texas A&M University - Kingsville, 2019.

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

Deep learning based strategies have shown exceptionally huge advancements in accuracy and quick decision making for applications in intelligent or autonomous driving vehicles.Pedestrian recognition, having applications in autonomous or intelligent vehicles, is one of the vital applications of object detection, an area of basic ongoing research in computer vision. From The last couple of decades, pedestrian recognition has played a vital role in numerous real time applications such as collision avoidance systems for smart or intelligent vehicles, intelligent observation cameras, and domestic security frameworks. The proposed methodology of this work suggests utilizing a dash camera to support a model that identifies all humans that might come in the way of a moving autonomous or driven vehicle from images captured with the dash camera. This algorithm proposed model is based on TensorFlow Human Detection API and is compared to the Histograms of Oriented Gradients (HOG) for human detection. Based on the research, the accuracy and efficiency of the proposed model to detect human was much better than to the other models. The defined method in this paper had an accuracy of around 98% as compared to 84% for the Histograms of Oriented Gradients detection model. The proposed model was faster on average as compared to the Histograms of Oriented Gradients detection model for any given picture on the same framework for training to testing because the proposed model took 11 seconds on average for processing one picture whereas the HOG method took more than 40 seconds on average. The proposed model gives “boxes” as outputs around the humans detected in the images and the result of this model is that it is accurately able to recognize pedestrians with the high accuracy of 98%. The samples on which the algorithm was tested are low-quality pictures taken from low-cost cameras, needing exceptionally less computing power, obviating need for expensive components. This low-cost proposed system will thus permit a dash-cam based system fitted with pedestrian detection technology in vehicles to be implemented for automatic vehicle and self-driving vehicle applications.

ISBN: 9781085773799Subjects--Topical Terms:

199325
Computer science.

Pedestrian Detection Using Deep Learning Through A Dashcam.
LDR:03220nmm a2200313 4500 001 570776
005 20200514111956.5
008 200901s2019 ||||||||||||||||| ||eng d
020 $a 9781085773799
035 $a (MiAaPQ)AAI13885475
035 $a AAI13885475
040 $a MiAaPQ $c MiAaPQ
100 1 $a Trivedi, Harshil Pareshkumar. $3 857471
245 1 0 $a Pedestrian Detection Using Deep Learning Through A Dashcam.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 81-04.
500 $a Advisor: Goyal, Ayush;Hicks, David.
502 $a Thesis (M.S.)--Texas A&M University - Kingsville, 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Deep learning based strategies have shown exceptionally huge advancements in accuracy and quick decision making for applications in intelligent or autonomous driving vehicles.Pedestrian recognition, having applications in autonomous or intelligent vehicles, is one of the vital applications of object detection, an area of basic ongoing research in computer vision. From The last couple of decades, pedestrian recognition has played a vital role in numerous real time applications such as collision avoidance systems for smart or intelligent vehicles, intelligent observation cameras, and domestic security frameworks. The proposed methodology of this work suggests utilizing a dash camera to support a model that identifies all humans that might come in the way of a moving autonomous or driven vehicle from images captured with the dash camera. This algorithm proposed model is based on TensorFlow Human Detection API and is compared to the Histograms of Oriented Gradients (HOG) for human detection. Based on the research, the accuracy and efficiency of the proposed model to detect human was much better than to the other models. The defined method in this paper had an accuracy of around 98% as compared to 84% for the Histograms of Oriented Gradients detection model. The proposed model was faster on average as compared to the Histograms of Oriented Gradients detection model for any given picture on the same framework for training to testing because the proposed model took 11 seconds on average for processing one picture whereas the HOG method took more than 40 seconds on average. The proposed model gives “boxes” as outputs around the humans detected in the images and the result of this model is that it is accurately able to recognize pedestrians with the high accuracy of 98%. The samples on which the algorithm was tested are low-quality pictures taken from low-cost cameras, needing exceptionally less computing power, obviating need for expensive components. This low-cost proposed system will thus permit a dash-cam based system fitted with pedestrian detection technology in vehicles to be implemented for automatic vehicle and self-driving vehicle applications.
590 $a School code: 1187.
650 4 $a Computer science. $3 199325
650 4 $a Artificial intelligence. $3 194058
690 $a 0984
690 $a 0800
710 2 $a Texas A&M University - Kingsville. $b Electrical Engineering and Computer Science. $3 857472
773 0 $t Masters Abstracts International $g 81-04.
790 $a 1187
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885475