Monitoring the crowd of people by deep learning enabled image analytics
There is a great demand for crowd counting in some practical applications nowadays, such as traffic monitoring, traffic management, sports events and political meetings. In some cases, it is extremely important to obtain information on the number of people. In recent years, many methods and network...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/150321 |
| _version_ | 1826129244913139712 |
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| author | Li, Jiani |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Li, Jiani |
| author_sort | Li, Jiani |
| collection | NTU |
| description | There is a great demand for crowd counting in some practical applications nowadays, such as traffic monitoring, traffic management, sports events and political meetings. In some cases, it is extremely important to obtain information on the number of people. In recent years, many methods and network models for calculating population density have been proposed and made significant progress. However, due to the uneven distribution, high congestion, chaos and occlusion, the effect of the traditional method is not ideal. And the display of the density map is more suitable to meet the demand of real applications. The convolutional neural network can perform well regression and a density map of crowd can be generated by taking the entire image as the input. Based on this method, the functions of accurate crowd statistics and high-quality density map generation are researched and implemented in this project, and a crowd monitoring system based on deep machine learning was developed. |
| first_indexed | 2024-10-01T07:37:48Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/150321 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:37:48Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1503212023-07-04T17:01:31Z Monitoring the crowd of people by deep learning enabled image analytics Li, Jiani Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering There is a great demand for crowd counting in some practical applications nowadays, such as traffic monitoring, traffic management, sports events and political meetings. In some cases, it is extremely important to obtain information on the number of people. In recent years, many methods and network models for calculating population density have been proposed and made significant progress. However, due to the uneven distribution, high congestion, chaos and occlusion, the effect of the traditional method is not ideal. And the display of the density map is more suitable to meet the demand of real applications. The convolutional neural network can perform well regression and a density map of crowd can be generated by taking the entire image as the input. Based on this method, the functions of accurate crowd statistics and high-quality density map generation are researched and implemented in this project, and a crowd monitoring system based on deep machine learning was developed. Master of Science (Signal Processing) 2021-06-08T12:44:53Z 2021-06-08T12:44:53Z 2021 Thesis-Master by Coursework Li, J. (2021). Monitoring the crowd of people by deep learning enabled image analytics. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150321 https://hdl.handle.net/10356/150321 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering Li, Jiani Monitoring the crowd of people by deep learning enabled image analytics |
| title | Monitoring the crowd of people by deep learning enabled image analytics |
| title_full | Monitoring the crowd of people by deep learning enabled image analytics |
| title_fullStr | Monitoring the crowd of people by deep learning enabled image analytics |
| title_full_unstemmed | Monitoring the crowd of people by deep learning enabled image analytics |
| title_short | Monitoring the crowd of people by deep learning enabled image analytics |
| title_sort | monitoring the crowd of people by deep learning enabled image analytics |
| topic | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/150321 |
| work_keys_str_mv | AT lijiani monitoringthecrowdofpeoplebydeeplearningenabledimageanalytics |