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).

Modelling events is one of the key problems in dynamic scene analysis when salient and autonomous visual changes occuring in a scene need to be characterised effectively as meaningful events. We propose a new approach for modelling such temporal events based on the local intensity temporal history of pixels. The method provides a computationally very effective temporal measure for detecting autonomous events. Events are represented and detected first at the pixel level and then at a blob level (grouped pixels) autonomously. The Expectation-Maximisation (EM) algorithm is employed to cluster events with automatic model order selection using modified Minimum Description Length (MDL). Experiments are presented to demonstrate that meaningful clusters of blob-level events can be formed without object segmentation and tracking. 1

The problems of measuring the performance of a highly parallel multiple processor system, such as the 4096 element ICL Distributed Array Processor are presented in relation to the conventional methods used for serial processors; this is preceded by a brief description of the DAP hardware in order to. provide a framework for the discussion, together with some of the resulting implications for algorithm design. The importance of choosing algorithms for parallel computation in such a way as to make the best use of the parallelism of the hardware for the problem to be solved is discussed, and examples are given of parallel and hybrid algorithms—in the latter a mixture of serial and parallel techniques are used. A method of comparison of performance at the problem solving level is presented, which is illustrated by results obtained by DAP users studying problems which arise in a wide range of application areas.