Online Action Detection

14 papers with code • 2 benchmarks • 2 datasets

Online action detection is the task of predicting the action as soon as it happens in a streaming video without access to video frames in the future.

Most implemented papers

Temporal Recurrent Networks for Online Action Detection

xumingze0308/TRN.pytorch ICCV 2019

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.

Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks

seanmcgovern21/Machine-Learning-CS539 19 Apr 2016

In this paper, we study the problem of online action detection from streaming skeleton data.

Learning to Discriminate Information for Online Action Detection

hjeun/idu CVPR 2020

For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others.

A Comprehensive Study on Temporal Modeling for Online Action Detection

wangwen39/Temporal-Modeling-Methods-for-OAD 21 Jan 2020

Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years.

The Instantaneous Accuracy: a Novel Metric for the Problem of Online Human Behaviour Recognition in Untrimmed Videos

gramuah/ia 22 Mar 2020

The problem of Online Human Behaviour Recognition in untrimmed videos, aka Online Action Detection (OAD), needs to be revisited.

Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol

gramuah/ia 26 Mar 2020

Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario.

Two-Stream AMTnet for Action Detection

gurkirt/AMTNet 3 Apr 2020

This is achieved by augmenting the previous Action Micro-Tube (AMTnet) action detection framework in three distinct ways: by adding a parallel motion stIn this paper, we propose a new deep neural network architecture for online action detection, termed ream to the original appearance one in AMTnet; (2) in opposition to state-of-the-art action detectors which train appearance and motion streams separately, and use a test time late fusion scheme to fuse RGB and flow cues, by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time; (3) by introducing an online action tube generation algorithm which works at video-level, and in real-time (when exploiting only appearance features).

Temporally smooth online action detection using cycle-consistent future anticipation

YHKimGithub/FATSnet 16 Apr 2021

Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end.

OadTR: Online Action Detection with Transformers

wangxiang1230/OadTR ICCV 2021

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.

Long Short-Term Transformer for Online Action Detection

amazon-research/long-short-term-transformer NeurIPS 2021

We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data.