Crowd-based people detection using deep learning

Human detection is a hot research field in computer vision. Its purpose is to detect humans in images or video sequences and provide precise location by using bounding boxes. One major challenge is to detect people in crowded environments. Specifically, this challenge is broken into two main diff...

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Main Author: Chen, Lei
Other Authors: Yap Kim Hui
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/155650
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1556502023-07-04T17:36:15Z Crowd-based people detection using deep learning Chen, Lei Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Electrical and electronic engineering Human detection is a hot research field in computer vision. Its purpose is to detect humans in images or video sequences and provide precise location by using bounding boxes. One major challenge is to detect people in crowded environments. Specifically, this challenge is broken into two main difficulties: multi-scale problems and occlusion problems. Most object detection algorithms have low accuracy for small object detection and identification. When objects are occluded, the features will overlap, making it hard to determine a correct outcome and leading to false or missed detection. This project first reviewed an extensive list of literature related to object detection based on handcraft and deep learning methods. Then, two state of art neural networks were introduced (EfficientDet and YOLOv5), and through further analysis, I analyzed the components and the thesis and the subsequent source codes, deduced the complete network structure, and explained the specific implementation process of the critical parts. From this, I used the Crowd- Human data set to train the aforementioned networks, and got two object detection networks for people in crowded environments. Experimental results suggest that compared to Faster RCNN, EfficientDet yields detection speeds three times faster in crowded environments, whereas YOLOv5 provides the fastest detection speed while increasing AP by almost 18%. In addition, YOLOv5 solves the difficulty of detecting small-scale objects while maintaining high accuracy of objects in crowded environments. Overall, YOLOv5 can effectively detect people in crowded environments and prevent missed detection of hard-to-detect small-scale people. Its high detection accuracy and fast detection speed make an effective option for crowded environment scenarios. Keywords: Deep Learning, Object Detection, Human Detection. Master of Science (Signal Processing) 2022-03-10T04:20:40Z 2022-03-10T04:20:40Z 2021 Thesis-Master by Coursework Chen, L. (2021). Crowd-based people detection using deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155650 https://hdl.handle.net/10356/155650 en ISM-DISS-02451 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Chen, Lei
Crowd-based people detection using deep learning
description Human detection is a hot research field in computer vision. Its purpose is to detect humans in images or video sequences and provide precise location by using bounding boxes. One major challenge is to detect people in crowded environments. Specifically, this challenge is broken into two main difficulties: multi-scale problems and occlusion problems. Most object detection algorithms have low accuracy for small object detection and identification. When objects are occluded, the features will overlap, making it hard to determine a correct outcome and leading to false or missed detection. This project first reviewed an extensive list of literature related to object detection based on handcraft and deep learning methods. Then, two state of art neural networks were introduced (EfficientDet and YOLOv5), and through further analysis, I analyzed the components and the thesis and the subsequent source codes, deduced the complete network structure, and explained the specific implementation process of the critical parts. From this, I used the Crowd- Human data set to train the aforementioned networks, and got two object detection networks for people in crowded environments. Experimental results suggest that compared to Faster RCNN, EfficientDet yields detection speeds three times faster in crowded environments, whereas YOLOv5 provides the fastest detection speed while increasing AP by almost 18%. In addition, YOLOv5 solves the difficulty of detecting small-scale objects while maintaining high accuracy of objects in crowded environments. Overall, YOLOv5 can effectively detect people in crowded environments and prevent missed detection of hard-to-detect small-scale people. Its high detection accuracy and fast detection speed make an effective option for crowded environment scenarios. Keywords: Deep Learning, Object Detection, Human Detection.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Chen, Lei
format Thesis-Master by Coursework
author Chen, Lei
author_sort Chen, Lei
title Crowd-based people detection using deep learning
title_short Crowd-based people detection using deep learning
title_full Crowd-based people detection using deep learning
title_fullStr Crowd-based people detection using deep learning
title_full_unstemmed Crowd-based people detection using deep learning
title_sort crowd-based people detection using deep learning
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/155650
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