Name
Abstract | ||
|---|---|---|
We present a novel framework for recognizing repetitive sequential events performed by human actors with strong temporal dependencies and potential parallel overlap. Our solution incorporates sub-event (or primitive) detectors and a spatiotemporal model for sequential event changes. We develop an effective and efficient method to integrate primitives into a set of sequential events where strong temporal constraints are imposed on the ordering of the primitives. In particular the combination process is approached as an optimization problem. A specialized Viterbi algorithm is designed to learn and infer the target sequential events and handle the event overlap simultaneously. To demonstrate the effectiveness of the proposed framework, we report detailed quantitative analysis on a large set of cashier check-out activities in a retail store. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CVPRW.2009.5206644 | CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4 |
Keywords | Field | DocType |
data mining,optimization problem,visualization,viterbi algorithm,algorithm design and analysis,quantitative analysis,hidden markov models,detectors | Object detection,Computer vision,Pattern recognition,Visualization,Computer science,Artificial intelligence,Hidden Markov model,Detector,Optimization problem,Viterbi algorithm | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1063-6919 |
Citations | PageRank | References |
16 | 1.27 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quanfu Fan | 1 | 504 | 32.69 |
| Russell Bobbitt | 2 | 30 | 3.04 |
| Yun Zhai | 3 | 735 | 32.59 |
| Akira Yanagawa | 4 | 278 | 23.69 |
| Sharath Pankanti | 5 | 3542 | 292.65 |
| Arun Hampapur | 6 | 1106 | 209.27 |