Action Recognition In Videos
64 papers with code • 17 benchmarks • 17 datasets
Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
Benchmarks
These leaderboards are used to track progress in Action Recognition In Videos
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Libraries
Use these libraries to find Action Recognition In Videos models and implementationsDatasets
UCF101
Kinetics
HMDB51
ActivityNet
Kinetics 400
NTU RGB+D
THUMOS14
Something-Something V2
Sports-1M
Kinetics-600
Most implemented papers
Learning Spatiotemporal Features with 3D Convolutional Networks
facebookarchive/C3D
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ICCV 2015
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
yjxiong/temporal-segment-networks
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The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
SlowFast Networks for Video Recognition
facebookresearch/SlowFast
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ICCV 2019
We present SlowFast networks for video recognition.
Temporal Segment Networks for Action Recognition in Videos
yjxiong/temporal-segment-networks
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Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
Two-Stream Convolutional Networks for Action Recognition in Videos
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.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Towards Good Practices for Very Deep Two-Stream ConvNets
However, for action recognition in videos, the improvement of deep convolutional networks is not so evident.
Temporal Relational Reasoning in Videos
metalbubble/TRN-pytorch
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ECCV 2018
Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species.
Representation Flow for Action Recognition
piergiaj/representation-flow-cvpr19
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CVPR 2019
Our representation flow layer is a fully-differentiable layer designed to capture the `flow' of any representation channel within a convolutional neural network for action recognition.
