Vulnerable Road Users Detection using Convolutional Deep Feedforward Network
A new convolutional deep feedforward network (C-DFN) is proposed to detect vulnerable road users at 57.9% misclassification rate using Caltech Dataset. Instead of going deeper, three C-DFN is stacked to achieve 43.4% misclassification rate. Part-based C-DFN further reduces the rate of 42.5% to tackl...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/83745 |
| _version_ | 1848764603442397184 |
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| author | Lau, Mian Mian |
| author_facet | Lau, Mian Mian |
| author_sort | Lau, Mian Mian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A new convolutional deep feedforward network (C-DFN) is proposed to detect vulnerable road users at 57.9% misclassification rate using Caltech Dataset. Instead of going deeper, three C-DFN is stacked to achieve 43.4% misclassification rate. Part-based C-DFN further reduces the rate of 42.5% to tackle occlusion problem. In addition, investigation of adaptive activation functions are performed to understand the effect of saturated and non-saturated functions in mitigating the vanishing and exploding gradient issues of neural networks. |
| first_indexed | 2025-11-14T11:21:59Z |
| format | Thesis |
| id | curtin-20.500.11937-83745 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:21:59Z |
| publishDate | 2021 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-837452021-05-24T07:07:31Z Vulnerable Road Users Detection using Convolutional Deep Feedforward Network Lau, Mian Mian A new convolutional deep feedforward network (C-DFN) is proposed to detect vulnerable road users at 57.9% misclassification rate using Caltech Dataset. Instead of going deeper, three C-DFN is stacked to achieve 43.4% misclassification rate. Part-based C-DFN further reduces the rate of 42.5% to tackle occlusion problem. In addition, investigation of adaptive activation functions are performed to understand the effect of saturated and non-saturated functions in mitigating the vanishing and exploding gradient issues of neural networks. 2021 Thesis http://hdl.handle.net/20.500.11937/83745 Curtin University fulltext |
| spellingShingle | Lau, Mian Mian Vulnerable Road Users Detection using Convolutional Deep Feedforward Network |
| title | Vulnerable Road Users Detection using Convolutional Deep
Feedforward Network |
| title_full | Vulnerable Road Users Detection using Convolutional Deep
Feedforward Network |
| title_fullStr | Vulnerable Road Users Detection using Convolutional Deep
Feedforward Network |
| title_full_unstemmed | Vulnerable Road Users Detection using Convolutional Deep
Feedforward Network |
| title_short | Vulnerable Road Users Detection using Convolutional Deep
Feedforward Network |
| title_sort | vulnerable road users detection using convolutional deep
feedforward network |
| url | http://hdl.handle.net/20.500.11937/83745 |