Pedestrian detection using fast region-based convolutional neural network method

People are the center of all kinds of social activities. In real-life scenario, people are the most important objects of concern, such as pedestrians crossing the road, security inspections, etc. As a specific object detection method, pedestrian detection is the premise of vehicle-assisted driving,...

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Main Author: Wang, Xiaoxu
Other Authors: Lu Yilong
Format: Final Year Project
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/77882
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-778822023-07-07T15:56:45Z Pedestrian detection using fast region-based convolutional neural network method Wang, Xiaoxu Lu Yilong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering People are the center of all kinds of social activities. In real-life scenario, people are the most important objects of concern, such as pedestrians crossing the road, security inspections, etc. As a specific object detection method, pedestrian detection is the premise of vehicle-assisted driving, intelligent video surveillance, as well as human behavior analysis. With the continuous enhancement of hardware capabilities and related proposed algorithms, the performance of the pedestrian detection system is also constantly improving. Pedestrian detection possesses very important research significance and value, thus it has attracted wide attention of researchers as compared to the past. Singapore is one of the leading countries in Asia that invested numerous recourses in developing and researching on Artificial Intelligence (AI), such as pedestrian detection on self-governing vehicles. Many educational institution and research centers including NUS, NTU have developed their own prototypes of autonomous vehicle which have tested within the school campus. Indeed, Pedestrian detection will gradually become an indispensable research topic in Singapore, and in the near future, the research achievement will be applied to people's daily lives. In this thesis, the author will touch on the evolution of the new and old methods of object detection. By reviewing some literature researches with analysis, it is found that the existing Faster RCNN method has replaced the Fast RCNN method which was proposed earlier for this project. Furthermore, Faster RCNN method will be able to perform better detection function and deliver more accurate detection results for pedestrian detection. As a result, the construction and working principle of Faster R- CNN method which used in the experiment, and the corresponding dataset will be introduced in detail. Lastly, a demo pedestrian detection program will be developed based on Faster R-CNN platform with VGG16 network trained for detection on PASCAL VOC 2007. Comparisons and analysis on experiment results using different training models with variable training iterations will be discussed in this paper. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-07T07:25:33Z 2019-06-07T07:25:33Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77882 en Nanyang Technological University 74 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Wang, Xiaoxu
Pedestrian detection using fast region-based convolutional neural network method
description People are the center of all kinds of social activities. In real-life scenario, people are the most important objects of concern, such as pedestrians crossing the road, security inspections, etc. As a specific object detection method, pedestrian detection is the premise of vehicle-assisted driving, intelligent video surveillance, as well as human behavior analysis. With the continuous enhancement of hardware capabilities and related proposed algorithms, the performance of the pedestrian detection system is also constantly improving. Pedestrian detection possesses very important research significance and value, thus it has attracted wide attention of researchers as compared to the past. Singapore is one of the leading countries in Asia that invested numerous recourses in developing and researching on Artificial Intelligence (AI), such as pedestrian detection on self-governing vehicles. Many educational institution and research centers including NUS, NTU have developed their own prototypes of autonomous vehicle which have tested within the school campus. Indeed, Pedestrian detection will gradually become an indispensable research topic in Singapore, and in the near future, the research achievement will be applied to people's daily lives. In this thesis, the author will touch on the evolution of the new and old methods of object detection. By reviewing some literature researches with analysis, it is found that the existing Faster RCNN method has replaced the Fast RCNN method which was proposed earlier for this project. Furthermore, Faster RCNN method will be able to perform better detection function and deliver more accurate detection results for pedestrian detection. As a result, the construction and working principle of Faster R- CNN method which used in the experiment, and the corresponding dataset will be introduced in detail. Lastly, a demo pedestrian detection program will be developed based on Faster R-CNN platform with VGG16 network trained for detection on PASCAL VOC 2007. Comparisons and analysis on experiment results using different training models with variable training iterations will be discussed in this paper.
author2 Lu Yilong
author_facet Lu Yilong
Wang, Xiaoxu
format Final Year Project
author Wang, Xiaoxu
author_sort Wang, Xiaoxu
title Pedestrian detection using fast region-based convolutional neural network method
title_short Pedestrian detection using fast region-based convolutional neural network method
title_full Pedestrian detection using fast region-based convolutional neural network method
title_fullStr Pedestrian detection using fast region-based convolutional neural network method
title_full_unstemmed Pedestrian detection using fast region-based convolutional neural network method
title_sort pedestrian detection using fast region-based convolutional neural network method
publishDate 2019
url http://hdl.handle.net/10356/77882
_version_ 1772828053216428032