Early fire detection using enhanced optical flow analysis technique
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
| Những tác giả chính: | , |
|---|---|
| Tác giả khác: | |
| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
| Được phát hành: |
BRAC University
2018
|
| Những chủ đề: | |
| Truy cập trực tuyến: | http://hdl.handle.net/10361/10094 |
| id |
10361-10094 |
|---|---|
| record_format |
dspace |
| spelling |
10361-100942022-01-26T10:05:00Z Early fire detection using enhanced optical flow analysis technique Khondaker, Arnisha Khandaker, Arman Uddin, Jia Department of Computer Science and Engineering, BRAC University Fire detection Color segmentation YUV color space Shape analysis Optical flow analysis This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 50-52). This paper proposes a multi-stage fire detection model that consists of chromatic segmentation, shape analysis and differential optical flow estimation. At the initial phase, color segmentation is carried out which takes into account some of the existing state of the art color segmentation directives and employs a majority voting system among them to obtain the possible fire-like regions. The extracted sections are then passed onto a shape analyzer which verifies the authenticity of the candidate regions by inspecting the dynamics of shape. The distinctive change fire exhibits over time in its area-perimeter ratio is at the bedrock of this analyzer. Further evaluation is carried out by another analyzer that measures the turbulence of fire evaluated by an enhanced differential optical flow tracking algorithm. The Lucas-Kanade Tracking algorithm has been employed and extended to achieve this. The assessment of performance of the enhanced techniques was carried out by utilizing a versatile dataset containing videos from the MIVIA and Zenodo dataset. The dataset consists of a diverse array of different environments such as indoor, outdoor and forest fire. Some environments with no fire were also included to assess the rate of false positives. The model has successfully showed an improved accuracy of 95.62% when tested for the aforementioned dataset. Arnisha Khondaker Arman Khandaker B. Computer Science and Engineering 2018-05-09T04:21:54Z 2018-05-09T04:21:54Z 2018 2018-04 Thesis ID 14301068 ID 18141022 http://hdl.handle.net/10361/10094 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. 52 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Fire detection Color segmentation YUV color space Shape analysis Optical flow analysis |
| spellingShingle |
Fire detection Color segmentation YUV color space Shape analysis Optical flow analysis Khondaker, Arnisha Khandaker, Arman Early fire detection using enhanced optical flow analysis technique |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Uddin, Jia |
| author_facet |
Uddin, Jia Khondaker, Arnisha Khandaker, Arman |
| format |
Thesis |
| author |
Khondaker, Arnisha Khandaker, Arman |
| author_sort |
Khondaker, Arnisha |
| title |
Early fire detection using enhanced optical flow analysis technique |
| title_short |
Early fire detection using enhanced optical flow analysis technique |
| title_full |
Early fire detection using enhanced optical flow analysis technique |
| title_fullStr |
Early fire detection using enhanced optical flow analysis technique |
| title_full_unstemmed |
Early fire detection using enhanced optical flow analysis technique |
| title_sort |
early fire detection using enhanced optical flow analysis technique |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10094 |
| work_keys_str_mv |
AT khondakerarnisha earlyfiredetectionusingenhancedopticalflowanalysistechnique AT khandakerarman earlyfiredetectionusingenhancedopticalflowanalysistechnique |
| _version_ |
1814307115519442944 |