%0 Conference Proceedings %T Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis %+ Département Intelligence Ambiante et Systèmes Interactifs (DIASI) %A Dupont, C. %A Tobias, L. %A Luvison, B. %Z Conference of 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 ; Conference Date: 21 July 2017 Through 26 July 2017; Conference Code:130113 %< avec comité de lecture %B 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) %C Honolulu, United States %I IEEE Computer Society %V 2017-July %P 2184-2191 %8 2017-07-21 %D 2017 %R 10.1109/CVPRW.2017.271 %K Characterization %K Computer vision %K Pattern recognition %K Behaviour analysis %K Context independent %K Crowd analysis %K Crowd managements %K Deep architectures %K High complexity %K ITS applications %K Manual monitoring %K Behavioral research %Z Engineering Sciences [physics]Conference papers %X Crowd behaviour analysis is a challenging task in computer vision, mainly due to the high complexity of the interactions between groups and individuals. This task is particularly crucial given the magnitude of manual monitoring required for effective crowd management. Within this context, a key challenge is to conceive a highly generic, fine and context-independent characterisation of crowd behaviours. Since current datasets answer only partially to this problem, a new dataset is generated, with a total of 11 crowd motion patterns and over 6000 video clips with an average length of 100 frames per sequence. We establish the first baseline of crowd characterisation with an extensive evaluation on shallow and deep methods. This characterisation is expected to be useful in multiple crowd analysis circumstances, we present a new deep architecture for crowd characterisation and demonstrate its application in the context of anomaly classification. %G English %L cea-01831840 %U https://cea.hal.science/cea-01831840 %~ CEA %~ DRT %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ CEA-UPSAY-SACLAY %~ LIST %~ GS-ENGINEERING %~ GS-COMPUTER-SCIENCE