Detecting inexplicable behaviour

This paper presents a novel approach to the detection of unusual or interesting events in videos involving certain types of intentional behaviour, such as pedestrian scenes. The approach is not based upon a statistical measure of typicality, but upon building an understanding of the way people navigate towards a goal. The activity of agents moving around within the scene is evaluated based upon whether the behaviour in question is consistent with a simple model of goal-directed behaviour and a model of those goals and obstacles known to be in the scene. The advantages of such an approach are multiple: it handles the presence of movable obstacles (for example, parked cars) with ease; trajectories which have never before been presented to the system can be classified as explicable; and the technique as a whole has a prima facie psychological plausibility. A system based upon these principles is demonstrated in two scenes: a car-park, and in a foyer scenario 1.

08091 Abstracts Collection -- Logic and Probability for Scene Interpretation

From 25.2.2008 to Friday 29.2.2008, the Dagstuhl Seminar 08091 ``Logic and Probability for Scene Interpretation\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper

Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector.</p

Feature space analysis for human activity recognition in smart environments

Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets