Long-term Behaviour Recognition in Videos with Actor-focused Region Attention

Luca Ballan, Luca Ballan, Ombretta Strafforello, Ombretta Strafforello, Klamer Schutte

Abstract

Long-Term activities involve humans performing complex, minutes-long actions. Differently than in traditional action recognition, complex activities are normally composed of a set of sub-actions, that can appear in different order, duration, and quantity. These aspects introduce a large intra-class variability, that can be hard to model. Our approach aims to adaptively capture and learn the importance of spatial and temporal video regions for minutes-long activity classification. Inspired by previous work on Region Attention, our architecture embeds the spatio-temporal features from multiple video regions into a compact fixed-length representation. These features are extracted with a 3D convolutional backbone specially fine-tuned. Additionally, driven by the prior assumption that the most discriminative locations in the videos are centered around the human that is carrying out the activity, we introduce an Actor Focus mechanism to enhance the feature extraction both in training and inference phase. Our experiments show that the Multi-Regional fine-tuned 3D-CNN, topped with Actor Focus and Region Attention, largely improves the performance of baseline 3D architectures, achieving state-of-the-art results on Breakfast, a well known long-term activity recognition benchmark.

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Paper Citation


in Harvard Style

Ballan L., Strafforello O. and Schutte K. (2021). Long-term Behaviour Recognition in Videos with Actor-focused Region Attention.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 362-369. DOI: 10.5220/0010215803620369


in Bibtex Style

@conference{visapp21,
author={Luca Ballan and Ombretta Strafforello and Klamer Schutte},
title={Long-term Behaviour Recognition in Videos with Actor-focused Region Attention},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={362-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010215803620369},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Long-term Behaviour Recognition in Videos with Actor-focused Region Attention
SN - 978-989-758-488-6
AU - Ballan L.
AU - Strafforello O.
AU - Schutte K.
PY - 2021
SP - 362
EP - 369
DO - 10.5220/0010215803620369