IEEE Trans Pattern Anal Mach Intell - Identifying Rare and Subtle Behaviours: A Weakly Supervised Joint Topic Model.

Tópicos

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Resumo

One of the most interesting and desired capabilities for automated video behaviour analysis is the identification of rarely occurring and subtle behaviours. This is of practical value because dangerous or illegal activities often have few or possibly only one prior example to learn from, and are often subtle. Rare and subtle behaviour learning is challenging for two reasons: (1) contemporary modeling approaches require more data and supervision than may be available and (2) the most interesting and potentially critical rare behaviours are often visually subtle - occurring among more obvious typical behaviours or being defined by only small spatio-temporal deviations from typical behaviours. In this paper we introduce a novel weakly-supervised joint topic model which addresses these issues. Specifically we introduce a multi-class topic model with partially shared latent structure and associated learning and inference algorithms. These contributions will permit modeling of behaviours from as few as one example, even without localisation by the user and when occurring in clutter; and subsequent classification and localisation such behaviours online and in real time. We extensively validate our approach on two standard public-space datasets where it clearly outperforms a batch of contemporary alternatives.

Resumo Limpo

one interest desir capabl autom video behaviour analysi identif rare occur subtl behaviour practic valu danger illeg activ often possibl one prior exampl learn often subtl rare subtl behaviour learn challeng two reason contemporari model approach requir data supervis may avail interest potenti critic rare behaviour often visual subtl occur among obvious typic behaviour defin small spatiotempor deviat typic behaviour paper introduc novel weaklysupervis joint topic model address issu specif introduc multiclass topic model partial share latent structur associ learn infer algorithm contribut will permit model behaviour one exampl even without localis user occur clutter subsequ classif localis behaviour onlin real time extens valid approach two standard publicspac dataset clear outperform batch contemporari altern

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