37,080 research outputs found
Two-Stream Convolutional Networks for Action Recognition in Videos
We investigate architectures of discriminatively trained deep Convolutional
Networks (ConvNets) for action recognition in video. The challenge is to
capture the complementary information on appearance from still frames and
motion between frames. We also aim to generalise the best performing
hand-crafted features within a data-driven learning framework.
Our contribution is three-fold. First, we propose a two-stream ConvNet
architecture which incorporates spatial and temporal networks. Second, we
demonstrate that a ConvNet trained on multi-frame dense optical flow is able to
achieve very good performance in spite of limited training data. Finally, we
show that multi-task learning, applied to two different action classification
datasets, can be used to increase the amount of training data and improve the
performance on both.
Our architecture is trained and evaluated on the standard video actions
benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of
the art. It also exceeds by a large margin previous attempts to use deep nets
for video classification
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
The paucity of videos in current action classification datasets (UCF-101 and
HMDB-51) has made it difficult to identify good video architectures, as most
methods obtain similar performance on existing small-scale benchmarks. This
paper re-evaluates state-of-the-art architectures in light of the new Kinetics
Human Action Video dataset. Kinetics has two orders of magnitude more data,
with 400 human action classes and over 400 clips per class, and is collected
from realistic, challenging YouTube videos. We provide an analysis on how
current architectures fare on the task of action classification on this dataset
and how much performance improves on the smaller benchmark datasets after
pre-training on Kinetics.
We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on
2D ConvNet inflation: filters and pooling kernels of very deep image
classification ConvNets are expanded into 3D, making it possible to learn
seamless spatio-temporal feature extractors from video while leveraging
successful ImageNet architecture designs and even their parameters. We show
that, after pre-training on Kinetics, I3D models considerably improve upon the
state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%
on UCF-101.Comment: Removed references to mini-kinetics dataset that was never made
publicly available and repeated all experiments on the full Kinetics datase
Encouraging LSTMs to Anticipate Actions Very Early
In contrast to the widely studied problem of recognizing an action given a
complete sequence, action anticipation aims to identify the action from only
partially available videos. As such, it is therefore key to the success of
computer vision applications requiring to react as early as possible, such as
autonomous navigation. In this paper, we propose a new action anticipation
method that achieves high prediction accuracy even in the presence of a very
small percentage of a video sequence. To this end, we develop a multi-stage
LSTM architecture that leverages context-aware and action-aware features, and
introduce a novel loss function that encourages the model to predict the
correct class as early as possible. Our experiments on standard benchmark
datasets evidence the benefits of our approach; We outperform the
state-of-the-art action anticipation methods for early prediction by a relative
increase in accuracy of 22.0% on JHMDB-21, 14.0% on UT-Interaction and 49.9% on
UCF-101.Comment: 13 Pages, 7 Figures, 11 Tables. Accepted in ICCV 2017. arXiv admin
note: text overlap with arXiv:1611.0552
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