caa a22 4500 467914451 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20060401xx s 000 0 eng 10.1007/s11263-006-4329-6 doi (NATIONALLICENCE)springer-10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour [Elektronische Daten] [Tao Xiang, Shaogang Gong] In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz's Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM). Springer Science + Business Media, Inc., 2006 dynamic scene modelling nationallicence graph models nationallicence discrete event recognition nationallicence activity representation nationallicence behaviour recognition nationallicence dynamic probabilistic networks nationallicence Bayesian model selection nationallicence Xiang Tao Department of Computer Science, Queen Mary, University of London, E1 4NS, UK aut Gong Shaogang Department of Computer Science, Queen Mary, University of London, E1 4NS, UK aut International Journal of Computer Vision Kluwer Academic Publishers 67/1(2006-04-01), 21-51 0920-5691 67:1<21 2006 67 11263 https://doi.org/10.1007/s11263-006-4329-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-4329-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Xiang Tao Department of Computer Science, Queen Mary, University of London, E1 4NS, UK aut NATIONALLICENCE 700 1- Gong Shaogang Department of Computer Science, Queen Mary, University of London, E1 4NS, UK aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 67/1(2006-04-01), 21-51 0920-5691 67:1<21 2006 67 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer