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Abstract: A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]