Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups / people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behaviors of individuals in a crowd (object based) and using this knowledge to make deductions regarding the crowd behavior and analyzing the crowd as a whole (holistic based). In this work, a holistic approach is used to develop a real-time abnormality detection in crowds using scale invariant feature transform (SIFT) based features and unsupervised machine learning techniques.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614659/index.pdf |
| Date | 01 September 2012 |
| Creators | Guler, Puren |
| Contributors | Temizel, Alptekin |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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