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Searching for complex human activities with no visual examples
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AI
DOI：10.1007/s11263-008-0142-8.png)
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摘要
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We describe a method of representing human activities that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across space and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing.
Keyword:
human action recognition
video retrieval
activity
HMM
motion capture
期刊
IF:
9.3
论文数:
3.9K
被引数:
2.8W
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