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Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian inference to the training stage, where unsupervised clustering is performed to extract semantically meaningful activities from the scene. In the testing stage, we use discriminative classifiers, taking advantage of their relative simplicity and fast inference. Experiments on publicly available data-sets show that our approach is comparable in classification accuracy to state-of-the-art methods and provides a significant speed-up in the testing phase.

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Abstract-In various real-world applications of distributed and multi-view vision systems, ability to learn unseen actions in an online fashion is paramount, as most of the actions are not known or sufficient training data is not available at design time. We propose a novel approach which combines the unsupervised learning capabilities of Hierarchical Dirichlet Processes (HDP) with Temporal Self-Similarity Maps (SSM) representations, which have been shown to be suitable for aggregating multiview information without further model knowledge. Furthermore, the HDP model, being almost completely data-driven, provides us with a system that works almost "out-of-the-box". Various experiments performed on the extensive JAR-AIBO dataset show promising results, with clustering accuracies up to 60% for a 56-class problem.

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