Visual event recognition in videos
The report provides a detailed documentation on the methods implemented and evaluations carried out in this project. The project aims to create a framework with an efficient classifier for visual event recognition in videos. Firstly, a dataset of videos made up of six classes of events were obta...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2012
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| Online Access: | http://hdl.handle.net/10356/48724 |
| _version_ | 1811681154434072576 |
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| author | Chan, Kerlina Pei Min. |
| author2 | Xu Dong |
| author_facet | Xu Dong Chan, Kerlina Pei Min. |
| author_sort | Chan, Kerlina Pei Min. |
| collection | NTU |
| description | The report provides a detailed documentation on the methods implemented and evaluations carried out in this project. The project aims to create a framework with an efficient classifier for visual event recognition in videos.
Firstly, a dataset of videos made up of six classes of events were obtained from the Kodak database. Next, the videos are divided into training and testing sets manually. Thereafter, space time interest points feature extraction method was used to extract interest points for all videos.
Subsequently, K-mean clustering was used to determine the optimal visual words clusters. For classification use, histograms were formed based on the optimal clusters. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were the two classification methods implemented in this project.
Finally, the performance of the classifiers was evaluated. The best classifier will be selected to apply in the framework. A user friendly graphical user interface (GUI) was created to implement with the framework for visual event recognition in videos. |
| first_indexed | 2024-10-01T03:36:26Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/48724 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:36:26Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/487242023-03-03T20:33:13Z Visual event recognition in videos Chan, Kerlina Pei Min. Xu Dong School of Computer Engineering Centre for Multimedia and Network Technology DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision The report provides a detailed documentation on the methods implemented and evaluations carried out in this project. The project aims to create a framework with an efficient classifier for visual event recognition in videos. Firstly, a dataset of videos made up of six classes of events were obtained from the Kodak database. Next, the videos are divided into training and testing sets manually. Thereafter, space time interest points feature extraction method was used to extract interest points for all videos. Subsequently, K-mean clustering was used to determine the optimal visual words clusters. For classification use, histograms were formed based on the optimal clusters. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were the two classification methods implemented in this project. Finally, the performance of the classifiers was evaluated. The best classifier will be selected to apply in the framework. A user friendly graphical user interface (GUI) was created to implement with the framework for visual event recognition in videos. Bachelor of Engineering (Computer Engineering) 2012-05-09T00:37:22Z 2012-05-09T00:37:22Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/48724 en Nanyang Technological University 65 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Chan, Kerlina Pei Min. Visual event recognition in videos |
| title | Visual event recognition in videos |
| title_full | Visual event recognition in videos |
| title_fullStr | Visual event recognition in videos |
| title_full_unstemmed | Visual event recognition in videos |
| title_short | Visual event recognition in videos |
| title_sort | visual event recognition in videos |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | http://hdl.handle.net/10356/48724 |
| work_keys_str_mv | AT chankerlinapeimin visualeventrecognitioninvideos |