Actom sequence models for efficient action detection

We address the problem of detecting actions, such as drinking or 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 characteristic for the action. Our model represents the temporal structure of actions as a sequence of histograms of actom-anchored visual features. Our representation, which can be seen as a temporally structured extension of the bag-of-features, is flexible, sparse and discriminative. We refer to our model as Actom Sequence Model (ASM). Training requires the annotation of actoms for action clips. At test time, actoms are detected 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 temporal action detection, “Coffee and Cigarettes” and the dataset of. We show that our ASM method outperforms the current state of the art in temporal action detection.

1. Introduction

We propose an approach for finding if and when an action is performed in a large video database. We focus on actions of short duration (typically a few seconds), like sitting down or opening a door, and several hours of real-world video data (e.g. movies). Our approach is based on decomposing actions into sequences of key atomic action units, which we refer to as actoms.