Search Results for author:
Found 7 papers, 2 papers with code
Group Activity Recognition using Unreliable Tracked Pose
Group activity recognition in video is a complex task due to the need for a model to recognise the actions of all individuals in the video and their complex interactions.
Pose is all you need: The pose only group activity recognition system (POGARS)
We introduce a novel deep learning based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity.
Ranked #6 on
Group Activity Recognition
on Volleyball
3D Human Pose Estimation with 2D Marginal Heatmaps
Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem.
Ranked #46 on
3D Human Pose Estimation
on MPI-INF-3DHP
Numerical Coordinate Regression with Convolutional Neural Networks
We study deep learning approaches to inferring numerical coordinates for points of interest in an input image.
Ranked #29 on
Pose Estimation
on MPII Human Pose
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks
Most research has been focused on action recognition and using it to classify many clips in continuous video for action localisation.
Extraction and Classification of Diving Clips from Continuous Video Footage
Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes.
Representing and Discovering Adversarial Team Behaviors Using Player Roles
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.
