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Found 8 papers, 4 papers with code
FenceNet: Fine-grained Footwork Recognition in Fencing
FenceNet takes 2D pose data as input and classifies actions using a skeleton-based action recognition approach that incorporates temporal convolutional networks to capture temporal information.
Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning
Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics.
Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation
In keypoint estimation tasks such as human pose estimation, heatmap-based regression is the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer from quantization error and require excessive computation to generate and post-process.
Ranked #9 on
Pose Estimation
on CrowdPose
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera
In the primary dataset containing 15k images captured from a face-on view of the dartboard using a smartphone, DeepDarts predicted the total score correctly in 94. 7% of the test images.
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer
Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks.
Ranked #1 on
Multi-Person Pose Estimation
on MS COCO
(Validation AP metric)
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance.
GolfDB: A Video Database for Golf Swing Sequencing
The golf swing is a complex movement requiring considerable full-body coordination to execute proficiently.
STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection
As such, there has been recent interest on human action recognition using low-cost, readily-available RGB cameras via deep convolutional neural networks.
Ranked #5 on
Multimodal Activity Recognition
on UTD-MHAD
