Title :
Temporal Poselets for Collective Activity Detection and Recognition
Author :
Nabi, M. ; Del Bue, Alessio ; Murino, Vittorio
Author_Institution :
Pattern Anal. & Comput. Vision (PAVIS), Ist. Italiano di Tecnol. (IIT), Genoa, Italy
Abstract :
Detection and recognition of collective human activities are important modules of any system devoted to high level social behavior analysis. In this paper, we present a novel semantic-based spatio-temporal descriptor which can cope with several interacting people at different scales and multiple activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded environments - the scenario most difficult to analyse because of occlusions. In particular, we extend the Pose let detector approach by defining a descriptor based on Pose let activation patterns over time, named TPOS. We will show that this descriptor can effectively tackle complex real scenarios allowing to detect humans in the scene, to localize (in space-time) human activities, and perform collective group activity recognition in a joint manner, reaching state-of-the-art results.
Keywords :
gesture recognition; video signal processing; TPOS; collective human activity detection; collective human activity recognition; high level social behavior analysis; human motion interaction; novel semantic-based spatio-temporal descriptor; poselet activation patterns; temporal poselet detector approach; Context; Detectors; Feature extraction; Semantics; Torso; Vectors; Video sequences; Activity Detection; Activity Recognition; Pose estimation; Poselet;
Conference_Titel :
Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCVW.2013.71