RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild
Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: 'Pointing'04' (aiming at covering a wide range of poses) and 'Annotated Facial Landmarks in the Wild' (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles (~4.4{\deg} average error on the test partition). Also, given its low inference time (~6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). * Code available at: https://github.com/rafabs97/headpose_final * Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE
Code (1)
Tasks
GPUHead Pose EstimationPose EstimationSimilar Papers 제목 키워드 기반
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet
Ensemble learning is a general technique to improve accuracy in machine learning. However, the heavy computation of a ConvNets ensemble limits its usage in deep learning. In this paper, we present Group Ensemble Network …
Action RecognitionEnsemble Learningobject-detectionObject DetectionConvolutional Neural Networks Applied to House Numbers Digit Classification
We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular visi…
ClassificationGeneral ClassificationHCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait
In this paper, we propose a novel deep learning method based on a new Hybrid ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD) from gait data. We adopt a two-step approach by dividing the prob…
severity predictionSingle-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as…
AutoMLGPUHyperparameter Optimizationimage-classification+2Transfer Learning Based on AdaBoost for Feature Selection from Multiple ConvNet Layer Features
Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is…
feature selectionTransfer Learning