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Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles./
Author:	Al-refai, Ghaith N.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	136 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
Contained By:	Dissertations Abstracts International80-07B.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10978385
ISBN:	9780438767690

Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles.
Al-refai, Ghaith N.

Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 136 p.

Source: Dissertations Abstracts International, Volume: 80-07, Section: B.

Thesis (Ph.D.)--Oakland University, 2018.

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

There has been a significant increase in road accidents in the past few years due to vehicle driving popularization around the world. More than half of road fatalities are attributed to pedestrians. In order to reduce the number of pedestrian fatalities, many safety features have been developed, such as Advanced Driver Assistance Systems (ADAS). In ADAS technology, many on-vehicle sensors are used to detect the surrounding of the vehicle, and then this information is used to prevent accidents by sending warnings to the driver or taking over control of the vehicle, such as applying a brake. Vision-based detection algorithms are a widely used technology in ADAS for pedestrian detection due to the rich information they provide and their low cost compared to other sensors. Vision-based pedestrian detection is done in the following steps: image acquisition, candidate generation, feature extraction, classification, and real-time object tracking. This work focused on advancing the candidate generation step of the process. Generating potential pedestrian candidates from the input image is an important step in the detection system, and it has a significant impact in the detection accuracy and the algorithm run-time. Classifying a large number of unnecessary candidates increases the processing requirements and may result in false positives. There are many approaches for candidate generation. The basic way is the sliding window approach, where the whole image is scanned by a sliding window at multiple scales of its original size. Other approaches are selective, and they focus on certain regions of interest in the image for candidate generation. The stereo-vision approach for candidate generation is an example of a selective approach, where a 3-D map is constructed for the image view, and then candidates are generated from certain regions based on depth values. The common disadvantage in the current candidate generation approaches is the generation of a large number of unnecessary candidates, many of which are static background objects. Also, some of these approaches are computationally expensive. This dissertation introduces a new approach for pedestrian detection in a road infrastructure environment. The main idea of the proposed approach is to utilize the image frames provided by the previous vehicles that passed by a certain road section to more intelligently generate candidates. Vehicle-to-Infrastructure (V2I) communication is used to transmit image frames collected by vehicles for a certain location to the infrastructure database. The images are processed in the infrastructure for background modeling and moving object extraction. Candidates are generated from the moving object regions in the processed image. The proposed approach eliminates the candidates generated from static background objects, such as trees and buildings. The proposed model improves the detection accuracy by reducing the false positives and reducing the run-time of the detection algorithms. The system architecture of the proposed model is provided. The infrastructure algorithms for background modeling and pedestrian detection are implemented, and the results are analyzed and compared to an industry standard reference algorithm.

ISBN: 9780438767690Subjects--Topical Terms:

1567821
Computer Engineering.
Subjects--Index Terms:

ADAS

Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 136 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Rawashdeh, Osamah A.
502 $a Thesis (Ph.D.)--Oakland University, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a There has been a significant increase in road accidents in the past few years due to vehicle driving popularization around the world. More than half of road fatalities are attributed to pedestrians. In order to reduce the number of pedestrian fatalities, many safety features have been developed, such as Advanced Driver Assistance Systems (ADAS). In ADAS technology, many on-vehicle sensors are used to detect the surrounding of the vehicle, and then this information is used to prevent accidents by sending warnings to the driver or taking over control of the vehicle, such as applying a brake. Vision-based detection algorithms are a widely used technology in ADAS for pedestrian detection due to the rich information they provide and their low cost compared to other sensors. Vision-based pedestrian detection is done in the following steps: image acquisition, candidate generation, feature extraction, classification, and real-time object tracking. This work focused on advancing the candidate generation step of the process. Generating potential pedestrian candidates from the input image is an important step in the detection system, and it has a significant impact in the detection accuracy and the algorithm run-time. Classifying a large number of unnecessary candidates increases the processing requirements and may result in false positives. There are many approaches for candidate generation. The basic way is the sliding window approach, where the whole image is scanned by a sliding window at multiple scales of its original size. Other approaches are selective, and they focus on certain regions of interest in the image for candidate generation. The stereo-vision approach for candidate generation is an example of a selective approach, where a 3-D map is constructed for the image view, and then candidates are generated from certain regions based on depth values. The common disadvantage in the current candidate generation approaches is the generation of a large number of unnecessary candidates, many of which are static background objects. Also, some of these approaches are computationally expensive. This dissertation introduces a new approach for pedestrian detection in a road infrastructure environment. The main idea of the proposed approach is to utilize the image frames provided by the previous vehicles that passed by a certain road section to more intelligently generate candidates. Vehicle-to-Infrastructure (V2I) communication is used to transmit image frames collected by vehicles for a certain location to the infrastructure database. The images are processed in the infrastructure for background modeling and moving object extraction. Candidates are generated from the moving object regions in the processed image. The proposed approach eliminates the candidates generated from static background objects, such as trees and buildings. The proposed model improves the detection accuracy by reducing the false positives and reducing the run-time of the detection algorithms. The system architecture of the proposed model is provided. The infrastructure algorithms for background modeling and pedestrian detection are implemented, and the results are analyzed and compared to an industry standard reference algorithm.
590 $a School code: 0446.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
653 $a ADAS
653 $a Candidate generation
653 $a Computer vision
653 $a Connected vehicles
653 $a Pedestrian detection
653 $a Vehicle safety
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a Oakland University. $b Engineering. $3 3288992
773 0 $t Dissertations Abstracts International $g 80-07B.
790 $a 0446
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10978385