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Deep Learning Algorithms for Background Subtraction and People Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning Algorithms for Background Subtraction and People Detection.
Author:	Tezcan, M. Ozan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2021
Description:	185 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Notes:	Advisor: Konrad, Janusz.
Contained By:	Dissertations Abstracts International83-04B.
Subject:	Electrical engineering.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647417
ISBN:	9798460476060

Deep Learning Algorithms for Background Subtraction and People Detection.
Tezcan, M. Ozan.

Deep Learning Algorithms for Background Subtraction and People Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 185 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--Boston University, 2021.

This item is not available from ProQuest Dissertations & Theses.

Video cameras are commonly used today in surveillance and security, autonomous driving and flying, manufacturing and healthcare. While different applications seek different types of information from the video streams, detecting changes and finding people are two key enablers for many of them. This dissertation focuses on both of these tasks: change detection, also known as background subtraction, and people detection from overhead fisheye cameras, an emerging research topic.Background subtraction has been thoroughly researched to date and the top-performing algorithms are data-driven and supervised. Crucially, during training these algorithms rely on the availability of some annotated frames from the video being tested. Instead, we propose a novel, supervised background-subtraction algorithm for unseen videos based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we introduce novel temporal and spatio-temporal data-augmentation methods. We also propose a cross-validation training/evaluation strategy for the largest change-detection dataset, CDNet-2014, that allows a fair and video-agnostic performance comparison of supervised algorithms. Overall, our algorithm achieves significant performance gains over state of the art in terms of F-measure, recall and precision. Furthermore, we develop a real-time variant of our algorithm with performance close to that of the state of the art.Owing to their large field of view, fisheye cameras mounted overhead are becoming a surveillance modality of choice for large indoor spaces. However, due to their top-down viewpoint and unique optics, standing people appear radially oriented and radially distorted in fisheye images. Therefore, traditional people detection, tracking and recognition algorithms developed for standard cameras do not perform well on fisheye images. To address this, we introduce several novel people-detection algorithms for overhead fisheye cameras. Our first two algorithms address the issue of radial body orientation by applying a rotating-window approach. This approach leverages a state-of-the-art object-detection algorithm trained on standard images and applies additional pre- and post-processing to detect radially-oriented people. Our third algorithm addresses both the radial body orientation and distortion by applying an end-to-end neural network with a novel angle-aware loss function and training on fisheye images. This algorithm outperforms the first two approaches and is two orders of magnitude faster. Finally, we introduce three spatio-temporal extensions of the end-to-end approach to deal with intermittent misses and false detections. In order to evaluate the performance of our algorithms, we collected, annotated and made publicly available four datasets composed of overhead fisheye videos. We provide a detailed analysis of our algorithms on these datasets and show that they significantly outperform the current state of the art.

ISBN: 9798460476060Subjects--Topical Terms:

454503
Electrical engineering.
Subjects--Index Terms:

Overhead fisheye cameras

Deep Learning Algorithms for Background Subtraction and People Detection.
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300 $a 185 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Konrad, Janusz.
502 $a Thesis (Ph.D.)--Boston University, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Video cameras are commonly used today in surveillance and security, autonomous driving and flying, manufacturing and healthcare. While different applications seek different types of information from the video streams, detecting changes and finding people are two key enablers for many of them. This dissertation focuses on both of these tasks: change detection, also known as background subtraction, and people detection from overhead fisheye cameras, an emerging research topic.Background subtraction has been thoroughly researched to date and the top-performing algorithms are data-driven and supervised. Crucially, during training these algorithms rely on the availability of some annotated frames from the video being tested. Instead, we propose a novel, supervised background-subtraction algorithm for unseen videos based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we introduce novel temporal and spatio-temporal data-augmentation methods. We also propose a cross-validation training/evaluation strategy for the largest change-detection dataset, CDNet-2014, that allows a fair and video-agnostic performance comparison of supervised algorithms. Overall, our algorithm achieves significant performance gains over state of the art in terms of F-measure, recall and precision. Furthermore, we develop a real-time variant of our algorithm with performance close to that of the state of the art.Owing to their large field of view, fisheye cameras mounted overhead are becoming a surveillance modality of choice for large indoor spaces. However, due to their top-down viewpoint and unique optics, standing people appear radially oriented and radially distorted in fisheye images. Therefore, traditional people detection, tracking and recognition algorithms developed for standard cameras do not perform well on fisheye images. To address this, we introduce several novel people-detection algorithms for overhead fisheye cameras. Our first two algorithms address the issue of radial body orientation by applying a rotating-window approach. This approach leverages a state-of-the-art object-detection algorithm trained on standard images and applies additional pre- and post-processing to detect radially-oriented people. Our third algorithm addresses both the radial body orientation and distortion by applying an end-to-end neural network with a novel angle-aware loss function and training on fisheye images. This algorithm outperforms the first two approaches and is two orders of magnitude faster. Finally, we introduce three spatio-temporal extensions of the end-to-end approach to deal with intermittent misses and false detections. In order to evaluate the performance of our algorithms, we collected, annotated and made publicly available four datasets composed of overhead fisheye videos. We provide a detailed analysis of our algorithms on these datasets and show that they significantly outperform the current state of the art.
590 $a School code: 0017.
650 4 $a Electrical engineering. $3 454503
650 4 $a Computer science. $3 199325
653 $a Overhead fisheye cameras
653 $a Video cameras
653 $a Surveillance and security
690 $a 0544
690 $a 0984
710 2 $a Boston University. $b Electrical & Computer Engineering ENG. $3 915736
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0017
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647417