Crowd detection from aerial images
The detection of crowd from surveillance imagery is important to monitor public places and to ensure public safety. Hence, this work proposes crowd detection from static image captured from Unmanned Aerial Vehicle. The proposed methodology consists of three steps: FAST feature extraction, Gray Level...
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| التنسيق: | أطروحة |
| اللغة: | English |
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2015
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| الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/48892/25/SitiErneeMdzAiniMFKE2015.pdf |
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my-utm-ep.488922020-07-05T06:48:38Z Crowd detection from aerial images 2015-01 Md. Zaini, Siti Ernee TK7885-7895 Computer engineer. Computer hardware The detection of crowd from surveillance imagery is important to monitor public places and to ensure public safety. Hence, this work proposes crowd detection from static image captured from Unmanned Aerial Vehicle. The proposed methodology consists of three steps: FAST feature extraction, Gray Level Co-Occurrence Matrix (GLCM) feature computation and the use of Support Vector Machine (SVM) for classification. The use of FAST corner detector is to obtain regions of interest where possible existence of crowd. The application of GLCM is to extract second order statistical texture features for texture analysis. The result of GLCM then, will be classified to crowd and non-crowd using SVM. For evaluation, ten different images were used taken in various crowd formation, event and location. The accuracy of the proposed method is obtained and the classification results are shown visually. 2015-01 Thesis http://eprints.utm.my/id/eprint/48892/ http://eprints.utm.my/id/eprint/48892/25/SitiErneeMdzAiniMFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86749 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| language |
English |
| topic |
TK7885-7895 Computer engineer Computer hardware |
| spellingShingle |
TK7885-7895 Computer engineer Computer hardware Md. Zaini, Siti Ernee Crowd detection from aerial images |
| description |
The detection of crowd from surveillance imagery is important to monitor public places and to ensure public safety. Hence, this work proposes crowd detection from static image captured from Unmanned Aerial Vehicle. The proposed methodology consists of three steps: FAST feature extraction, Gray Level Co-Occurrence Matrix (GLCM) feature computation and the use of Support Vector Machine (SVM) for classification. The use of FAST corner detector is to obtain regions of interest where possible existence of crowd. The application of GLCM is to extract second order statistical texture features for texture analysis. The result of GLCM then, will be classified to crowd and non-crowd using SVM. For evaluation, ten different images were used taken in various crowd formation, event and location. The accuracy of the proposed method is obtained and the classification results are shown visually. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Md. Zaini, Siti Ernee |
| author_facet |
Md. Zaini, Siti Ernee |
| author_sort |
Md. Zaini, Siti Ernee |
| title |
Crowd detection from aerial images |
| title_short |
Crowd detection from aerial images |
| title_full |
Crowd detection from aerial images |
| title_fullStr |
Crowd detection from aerial images |
| title_full_unstemmed |
Crowd detection from aerial images |
| title_sort |
crowd detection from aerial images |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Electrical Engineering |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2015 |
| url |
http://eprints.utm.my/id/eprint/48892/25/SitiErneeMdzAiniMFKE2015.pdf |
| _version_ |
1747817497234505728 |