Object detection and representation method for surveillance video indexing
The huge volume of videos produced by surveillance cameras has increased the demand for the fast and effective video surveillance indexing and retrieval systems. Although environmental condition such as light reflection, illumination changes, shadow, and occlusion can affect the indexing and retriev...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/55677/ http://psasir.upm.edu.my/id/eprint/55677/1/Object%20detection%20and%20representation%20method%20for%20surveillance%20video%20indexing.pdf |
| _version_ | 1848852868590731264 |
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| author | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Khalid, Fatimah Mustapha, Norwati |
| author_facet | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Khalid, Fatimah Mustapha, Norwati |
| author_sort | Chamasemani, Fereshteh Falah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The huge volume of videos produced by surveillance cameras has increased the demand for the fast and effective video surveillance indexing and retrieval systems. Although environmental condition such as light reflection, illumination changes, shadow, and occlusion can affect the indexing and retrieval result of any video surveillance system, nevertheless the use of reliable and robust object (blob) detection and representation methods can improve the performance of the system. This paper presents a video indexing module, which is part of a video surveillance indexing and retrieval framework, to overcome the above challenges. The proposed video indexing module is composed of seven components: background modeling, foreground extraction, blob detection, blob analysis, feature extraction, blob representation, and blob indexing. The experimental results showed that the selection of appropriate blob detection method could improve the performance of the system. Moreover, the experiments also demonstrated that the functionality of the proposed blob representation method was able to prevent the processing of redundant blobs' information. |
| first_indexed | 2025-11-15T10:44:55Z |
| format | Conference or Workshop Item |
| id | upm-55677 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:44:55Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-556772017-06-07T08:34:20Z http://psasir.upm.edu.my/id/eprint/55677/ Object detection and representation method for surveillance video indexing Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Khalid, Fatimah Mustapha, Norwati The huge volume of videos produced by surveillance cameras has increased the demand for the fast and effective video surveillance indexing and retrieval systems. Although environmental condition such as light reflection, illumination changes, shadow, and occlusion can affect the indexing and retrieval result of any video surveillance system, nevertheless the use of reliable and robust object (blob) detection and representation methods can improve the performance of the system. This paper presents a video indexing module, which is part of a video surveillance indexing and retrieval framework, to overcome the above challenges. The proposed video indexing module is composed of seven components: background modeling, foreground extraction, blob detection, blob analysis, feature extraction, blob representation, and blob indexing. The experimental results showed that the selection of appropriate blob detection method could improve the performance of the system. Moreover, the experiments also demonstrated that the functionality of the proposed blob representation method was able to prevent the processing of redundant blobs' information. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/55677/1/Object%20detection%20and%20representation%20method%20for%20surveillance%20video%20indexing.pdf Chamasemani, Fereshteh Falah and Affendey, Lilly Suriani and Khalid, Fatimah and Mustapha, Norwati (2015) Object detection and representation method for surveillance video indexing. In: 2015 IEEE 3rd International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2015), 24-25 Nov. 2015, Putrajaya, Malaysia. . 10.1109/ICSIMA.2015.7559038 |
| spellingShingle | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Khalid, Fatimah Mustapha, Norwati Object detection and representation method for surveillance video indexing |
| title | Object detection and representation method for surveillance video indexing |
| title_full | Object detection and representation method for surveillance video indexing |
| title_fullStr | Object detection and representation method for surveillance video indexing |
| title_full_unstemmed | Object detection and representation method for surveillance video indexing |
| title_short | Object detection and representation method for surveillance video indexing |
| title_sort | object detection and representation method for surveillance video indexing |
| url | http://psasir.upm.edu.my/id/eprint/55677/ http://psasir.upm.edu.my/id/eprint/55677/ http://psasir.upm.edu.my/id/eprint/55677/1/Object%20detection%20and%20representation%20method%20for%20surveillance%20video%20indexing.pdf |