A Bayesian Computer Vision System for Modeling Human Interaction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Activity recognition by integrating the physics of motion with a neuromorphic model of perception
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Query-based retrieval of complex activities using "strings of motion-words"
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Large-scale multimedia content analysis using scientific workflows
Proceedings of the 21st ACM international conference on Multimedia
Modeling multi-object interactions using "string of feature graphs"
Computer Vision and Image Understanding
Structured analysis of the ISI Atomic Pair Actions dataset using workflows
Pattern Recognition Letters
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In this paper, we focus on the problem of searching for complex activities involving multiple, interacting objects in video. We examine the dynamics of formation and dispersal of groups as well as their interactions with other groups and individuals. In order to establish a general formalism, we examine activities using relative distances in phase space via pairwise analysis of all objects. This allows us to characterize interactions directly by modeling multi-object activities with the Multiple Objects, Pairwise Analysis (MOPA) feature vector, which is based upon physical models of complex interactions in phase space; specifically, we model paired motion as a damped oscillator in phase space. We model and recognize more complex interactions by characterizing pairs which are correlated in phase space as groups. We show how this model can be used for recognition of complex activities on the standard CAVIAR, VIVID, and UCR Videoweb datasets capturing a variety of problem settings.