A Prospective Study on Sequence-Driven Temporal Sampling and Ego-Motion Compensation for Action Recognition in the EPIC-Kitchens Dataset

• URL: http://arxiv.org/abs/2008.11588v1
• Date: Wed, 26 Aug 2020 14:44:45 GMT
• Title: A Prospective Study on Sequence-Driven Temporal Sampling and Ego-Motion Compensation for Action Recognition in the EPIC-Kitchens Dataset
• Authors: Alejandro L\'opez-Cifuentes, Marcos Escudero-Vi\~nolo, Jes\'us Besc\'os
• Abstract summary: Action recognition is one of the top-challenging research fields in computer vision. ego-motion recorded sequences have become of important relevance. The proposed method aims to cope with it by estimating this ego-motion or camera motion.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action recognition is currently one of the top-challenging research fields in computer vision. Convolutional Neural Networks (CNNs) have significantly boosted its performance but rely on fixed-size spatio-temporal windows of analysis, reducing CNNs temporal receptive fields. Among action recognition datasets, egocentric recorded sequences have become of important relevance while entailing an additional challenge: ego-motion is unavoidably transferred to these sequences. The proposed method aims to cope with it by estimating this ego-motion or camera motion. The estimation is used to temporally partition video sequences into motion-compensated temporal \textit{chunks} showing the action under stable backgrounds and allowing for a content-driven temporal sampling. A CNN trained in an end-to-end fashion is used to extract temporal features from each \textit{chunk}, which are late fused. This process leads to the extraction of features from the whole temporal range of an action, increasing the temporal receptive field of the network.

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