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Video event detection framework on large-scale video data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Park, Dong-Jun |
| Copyright Year | 2011 |
| Abstract | Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. Video data present a unique challenge for the information retrieval community because properly representing video events is challenging. We propose a novel approach to analyze temporal aspects of video data. We consider video data as a sequence of images that forms a 3-dimensional spatiotemporal structure, and perform multiview orthographic projection to transform the video data into 2-dimensional representations. The projected views allow a unique way to represent video events and capture the temporal aspect of video data. We extract local salient points from 2D projection views and perform detection-via-similarity approach on a wide range of events against real-world surveillance data. We demonstrate that our example-based detection framework is competitive and robust. We also investigate synthetic example driven retrieval as a basis for query-by-example. Abstract Approved: Thesis SupervisorApproved: Thesis Supervisor Title and Department |
| File Format | PDF HTM / HTML |
| DOI | 10.17077/etd.ypfqd78c |
| Alternate Webpage(s) | https://ir.uiowa.edu/cgi/viewcontent.cgi?article=2734&context=etd |
| Alternate Webpage(s) | http://ir.uiowa.edu/cgi/viewcontent.cgi?article=2734&context=etd |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
