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Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks

https://doi.org/10.1109/access.2018.2817253

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Deep learning

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A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions

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Recognizing realistic actions from videos “in the wild”

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Learning a hierarchy of discriminative space-time neighborhood features for human action recognition

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Action recognition by dense trajectories

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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis

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Incremental Activity Modeling and Recognition in Streaming Videos

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Large-Scale Video Classification with Convolutional Neural Networks

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Beyond short snippets: Deep networks for video classification

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Convolutional Two-Stream Network Fusion for Video Action Recognition

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Deep Residual Learning for Image Recognition

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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

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Action Recognition with Improved Trajectories

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Learning to Track for Spatio-Temporal Action Localization

inferredcites →https://doi.org/10.1109/iccv.2015.510openalex-snapshot

Learning Spatiotemporal Features with 3D Convolutional Networks

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A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles

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Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos

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Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition

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Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System

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Learning Hierarchical Features for Scene Labeling

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3D Convolutional Neural Networks for Human Action Recognition

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Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

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Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition

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Reducing the Dimensionality of Data with Neural Networks

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ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Effective Approaches to Attention-based Neural Machine Translation

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UCF-101: A dataset of 101 human actions classes from videos in the wild

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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

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Two-Stream Convolutional Networks for Action Recognition in Videos

inferredcites →https://doi.org/10.48550/arxiv.1502.03044openalex-snapshot

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

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Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

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Effective Approaches to Attention-based Neural Machine Translation

inferredcites →https://doi.org/10.48550/arxiv.1511.04119openalex-snapshot

Action Recognition using Visual Attention

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Action Recognition with Image Based CNN Features

inferredcites →https://doi.org/10.48550/arxiv.1607.06416openalex-snapshot

Hierarchical Attention Network for Action Recognition in Videos

inferredcites →https://doi.org/10.5244/c.22.99openalex-snapshot

A Spatio-Temporal Descriptor Based on 3D-Gradients

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Evaluation of local spatio-temporal features for action recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Spiking neural network based on joint entropy of optical flow features for human action recognition

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Two-Stage Human Activity Recognition Using 2D-ConvNet.

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