A comparative performance analysis of accident anticipation with deep learning extractors
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2023
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10361-218102023-10-15T21:05:04Z A comparative performance analysis of accident anticipation with deep learning extractors Mostak, Alfi Mashab Neha, Nayna Jahan Mohiuddin, Azwaad Labiba Tabassum, Adiba Hossain, Muhammad Iqbal Abrar, Mohammed Abid Department of Computer Science and Engineering, Brac University Accident anticipation Deep learning Car Crash Dataset (CCD) Dashcam Accident Dataset (DAD) Traffic accidents Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-32). Accident anticipation has become a major focus to avert accidents or to minimize their impacts. Over the years, several network systems are being developed and applied in self-driving technology. Despite the fact that advancement in the autonomous industry is fast-growing, major efficiency is required in the network systems that are gradually emerging. Recent research has proposed a novel end-to-end dynamic spatial-temporal attention network (DSTA) by combining a Gated Recur- rent Unit (GRU) with spatial-temporal attention learning network, to identify an accident video in 4.87 seconds before the occurrence of the accident with 99.6% ac- curacy when tested on the Car Crash Dataset (CCD). However, DSTA has not been able to provide efficient results on the Dashcam Accident Dataset (DAD) dataset. Moreover, the GRU model integrated in the DSTA network has a weak information processing capability and low update efficiency amid several hidden layers. The decision-making process of the accident anticipation network may be understood using the high quality saliency maps produced by the Grad-CAM and XGradCAM approaches. In this paper, we evaluate that using Wide ResNet network enhances the performance mechanism of feature extraction to increase accident anticipation precision. This change improves the capacity to process information and the learning efficacy. In addition, we suggest employing a Gated Recurrent Unit (GRU) network which will serve as a prominent feature to train the model to recognize data’s sequential properties and apply patterns to forecast the following likely event. Hence, we plan to incorporate Wide ResNet50, a system for extracting features which will identify the vehicles at risk by using wider residual blocks. These neural networks generate labels for identifying hazardous conditions in driving environments in order to anticipate accidents. Alfi Mashab Mostak Nayna Jahan Neha Azwaad Labiba Mohiuddin Adiba Tabassum B.Sc. in Computer Science 2023-10-15T06:21:49Z 2023-10-15T06:21:49Z ©2022 2022-09-29 Thesis ID 22341078 ID 19101223 ID 19101032 ID 19101211 http://hdl.handle.net/10361/21810 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Accident anticipation Deep learning Car Crash Dataset (CCD) Dashcam Accident Dataset (DAD) Traffic accidents Machine learning |
| spellingShingle |
Accident anticipation Deep learning Car Crash Dataset (CCD) Dashcam Accident Dataset (DAD) Traffic accidents Machine learning Mostak, Alfi Mashab Neha, Nayna Jahan Mohiuddin, Azwaad Labiba Tabassum, Adiba A comparative performance analysis of accident anticipation with deep learning extractors |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Mostak, Alfi Mashab Neha, Nayna Jahan Mohiuddin, Azwaad Labiba Tabassum, Adiba |
| format |
Thesis |
| author |
Mostak, Alfi Mashab Neha, Nayna Jahan Mohiuddin, Azwaad Labiba Tabassum, Adiba |
| author_sort |
Mostak, Alfi Mashab |
| title |
A comparative performance analysis of accident anticipation with deep learning extractors |
| title_short |
A comparative performance analysis of accident anticipation with deep learning extractors |
| title_full |
A comparative performance analysis of accident anticipation with deep learning extractors |
| title_fullStr |
A comparative performance analysis of accident anticipation with deep learning extractors |
| title_full_unstemmed |
A comparative performance analysis of accident anticipation with deep learning extractors |
| title_sort |
comparative performance analysis of accident anticipation with deep learning extractors |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21810 |
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