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Structured learning for detection of social groups in crowd

Abstract: 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.


Citation:

Solera, Francesco; Calderara, Simone; Cucchiara, Rita "Structured learning for detection of social groups in crowd" 2013 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance : AVSS 2013 : August 27-30, 2013, Krako┬┤w, Poland, vol. 0, Krakov (PL), pp. 7 -12 , August 27-30 2013, 2013 DOI: 10.1109/AVSS.2013.6636608

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