IEEE Trans Pattern Anal Mach Intell - Temporal Localization of Actions with Actoms.

Tópicos

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Resumo

We address the problem of localizing actions, such as opening a door, in hours of challenging video data. We propose a model based on a sequence of atomic action units, termed "actoms", that are semantically meaningful and characteristic for the action. Our Actom Sequence Model (ASM) represents an action as a sequence of histograms of actom-anchored visual features, which can be seen as a temporally structured extension of the bag-of-features. Training requires the annotation of actoms for action examples. At test time, actoms are localized automatically based on a non-parametric model of the distribution of actoms, which also acts as a prior on an action's temporal structure. We present experimental results on two recent benchmarks for action localization: "Coffee and Cigarettes" and the "DLSBP" dataset. We also adapt our approach to a classification-by-localization set-up and demonstrate its applicability on the challenging "Hollywood 2" dataset. We show that our ASM method outperforms the current state of the art in temporal action localization, as well as baselines that localize actions with a sliding window method.

Resumo Limpo

address problem local action open door hour challeng video data propos model base sequenc atom action unit term actom semant meaning characterist action actom sequenc model asm repres action sequenc histogram actomanchor visual featur can seen tempor structur extens bagoffeatur train requir annot actom action exampl test time actom local automat base nonparametr model distribut actom also act prior action tempor structur present experiment result two recent benchmark action local coffe cigarett dlsbp dataset also adapt approach classificationbyloc setup demonstr applic challeng hollywood dataset show asm method outperform current state art tempor action local well baselin local action slide window method

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