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
The human action image and its application to motion recognition
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
The human action image and its application to motion recognition
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Large-scale multimedia content analysis using scientific workflows
Proceedings of the 21st ACM international conference on Multimedia
Structured analysis of the ISI Atomic Pair Actions dataset using workflows
Pattern Recognition Letters
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In this paper, we employ ideas grounded in physics to examine activities in video. We build the Multi-Resolution Phase Space (MRPS) descriptor, which is a set of feature descriptors that is able to represent complex activities in multiple domains directly from tracks without the need for different heuristics. MRPS is used to do single- and multi-object activity modelling in phase space, which consists of all possible values of the coordinates. The MRPS contains the Sethi Metric (S-Metric), the Hamiltonian Energy Signature (HES), and the Multiple Objects, Pairwise Analysis (MOPA) descriptors: the S-Metric is a distance metric which characterizes the global motion of the object, or the entire scene, with a single, scalar value; the HES is a scalar or multi-dimensional time-series that represents the motion of an object over the course of an activity using either the Hamiltonian or the S-Metric; and the MOPA contains phase space features for paired activities, in which we develop physical models of complex interactions in phase space (specifically, we model paired motion as a damped oscillator in phase space). Finally, we show the S-Metric is a proper distance measure over a metric space and prove its additivity; this allows use of the S-Metric as a distance measure as well as its use in the HES. Experimental validation of the theory is provided on the standard VIVID and UCR Videoweb datasets capturing a variety of problem settings: single agent actions, multi-agent actions, and aerial sequences, including video search.