Abstract | ||
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Group detection in crowds will play a key role in future behavior analysis surveillance systems. In this work we build a new Structural SVM-based learning framework able to solve the group detection task by exploiting annotated video data to deduce a sociologically motivated distance measure founded on Hall's proxemics and Granger's causality. We improve over state-of-the-art results even in the most crowded test scenarios, while keeping the classification time affordable for quasi-real time applications. A new scoring scheme specifically designed for the group detection task is also proposed. |
Year | DOI | Venue |
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2013 | 10.1109/AVSS.2013.6636608 | Advanced Video and Signal Based Surveillance |
Keywords | Field | DocType |
learning (artificial intelligence),object detection,support vector machines,video surveillance,Granger causality,Hall proxemics,SVM-based learning,behavior analysis surveillance system,crowd detection,quasi-real time application,scoring scheme,social group detection,sociologically motivated distance measure,structured learning,video data | Social group,Object detection,Crowds,Computer vision,Causality,Computer science,Support vector machine,Proxemics,Structured prediction,Scenario testing,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
10 | 0.50 | 10 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Francesco Solera | 1 | 10 | 0.84 |
Simone Calderara | 2 | 936 | 54.25 |
Rita Cucchiara | 3 | 4174 | 300.55 |