Outdoor Activity Recognition using Multi-Linked Temporal Processes

• Main Authors: Tao Xiang, Shaogang Gong, Dennis Parkinson
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: DML
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.7977; http://www.comp.leeds.ac.uk/bmvc2008/proceedings/2003/papers/19/paper019.pdf

We develop Dynamically Multi-Linked Hidden Markov Models (DML-HMMs) for interpreting group activities involving multiple objects captured in an outdoor scene. The models are based on the discovery of salient dynamic interlinks among multiple different object events. A layered hierarchical DML-HMM is built using Schwarz’s Bayesian Information Criterion (BIC) based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among different object events. Our experiments demonstrate that the performance of a DML-HMM on modelling group activities in a noisy outdoor scene is superior compared to that of a Coupled Hidden Markov Model (CHMM).