Navigational strategies in behaviour modelling Export

@article{dee09ai, abstract = {We propose a new method that treats visible human behaviour at the level of navigational strategies. By inferring intentions in terms of known goals, it is possible to explain the behaviour of people moving around within the field of view of a video camera. The approach presented here incorporates models of navigation from within psychology which are both simple and conceptually plausible, whilst providing good results in an event-detection application. The output is in the form of statements involving goals, such as ” Agent 25 went to exit 8 via sub-goals 34 and 21” for a given navigational strategy, an image representing the path through the scene, and an overall score for each trajectory. The central algorithm generates all plausible paths through the scene to known goal sites and then compares each path to the agent's actual trajectory thus finding the most likely explanation for their behaviour. Two navigational strategies are examined, shortest path and simplest path . Experimental results are presented for an outdoor car-park and an indoor foyer scene, and our method is found to produce psychologically plausible explanations in the majority of cases. We propose a novel approach to determining the effectiveness of event detection systems, and evaluate the method presented here against this new ground truth . This evaluation method uses human observers to judge the behaviour shown in various video clips, then uses these judgements in correlation with those of the software. We compare the method with a standard machine learning approach based on nearest neighbour. Finally we consider the application of such a system in a binary event-detection or behaviour filtering system.}, address = {Essex, UK}, author = {DEE,, H and HOGG,, D}, citeulike-article-id = {4688130}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1480812}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.artint.2008.10.011}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0004370208001483}, doi = {10.1016/j.artint.2008.10.011}, issn = {00043702}, journal = {Artificial Intelligence}, keywords = {behaviour, computer, intentionality, navigation, vision}, month = {February}, number = {2}, pages = {329--342}, posted-at = {2009-08-27 15:36:25}, priority = {0}, publisher = {Elsevier Science Publishers Ltd.}, title = {Navigational strategies in behaviour modelling}, url = {http://dx.doi.org/10.1016/j.artint.2008.10.011}, volume = {173}, year = {2009} }

TY - JOUR ID - dee09ai L3 - citeulike-article-id:4688130 N2 - We propose a new method that treats visible human behaviour at the level of navigational strategies. By inferring intentions in terms of known goals, it is possible to explain the behaviour of people moving around within the field of view of a video camera. The approach presented here incorporates models of navigation from within psychology which are both simple and conceptually plausible, whilst providing good results in an event-detection application. The output is in the form of statements involving goals, such as “Agent 25 went to exit 8 via sub-goals 34 and 21” for a given navigational strategy, an image representing the path through the scene, and an overall score for each trajectory. The central algorithm generates all plausible paths through the scene to known goal sites and then compares each path to the agent's actual trajectory thus finding the most likely explanation for their behaviour. Two navigational strategies are examined, shortest path and simplest path . Experimental results are presented for an outdoor car-park and an indoor foyer scene, and our method is found to produce psychologically plausible explanations in the majority of cases. We propose a novel approach to determining the effectiveness of event detection systems, and evaluate the method presented here against this new ground truth . This evaluation method uses human observers to judge the behaviour shown in various video clips, then uses these judgements in correlation with those of the software. We compare the method with a standard machine learning approach based on nearest neighbour. Finally we consider the application of such a system in a binary event-detection or behaviour filtering system. IS - 2 JF - Artificial Intelligence SN - 00043702 AD - Essex, UK EP - 342 TI - Navigational strategies in behaviour modelling PB - Elsevier Science Publishers Ltd. VL - 173 SP - 329 KW - behaviour KW - computer KW - intentionality KW - navigation KW - vision AU - DEE,, H AU - HOGG,, D PY - 2009/02// UR - http://dx.doi.org/10.1016/j.artint.2008.10.011 ER -