Search Results for author:
Found 37 papers, 29 papers with code
Multiple Toddler Tracking in Indoor Videos
Multiple toddler tracking (MTT) involves identifying and differentiating toddlers in video footage.
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis.
Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental Cue
Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development.
Automatic Infant Respiration Estimation from Video: A Deep Flow-based Algorithm and a Novel Public Benchmark
Respiration is a critical vital sign for infants, and continuous respiratory monitoring is particularly important for newborns.
SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation
Our work demonstrates the potential for synthetic data to overcome the challenge of limited annotated data in animal pose estimation.
A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants
Tested on our second, independent, and public NNS in-the-wild dataset, NNS recognition classification reaches 92. 3\% accuracy, and NNS segmentation achieves 90. 8\% precision and 84. 2\% recall.
Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis
We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical.
Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data
Here, we present a very data efficient strategy targeted for pose estimation in quadrupeds that requires only a small amount of real images from the target animal.
Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation
To address this problem, we present a temporally embedded 3D human body pose and shape estimation (TePose) method to improve the accuracy and temporal consistency of pose estimation in live stream videos.
Ranked #55 on
3D Human Pose Estimation
on 3DPW
Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations
Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments.
Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging
Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map.
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Factor analysis methods have been widely used in neuroimaging to transfer high dimensional imaging data into low dimensional, ideally interpretable representations.
InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild
We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders.
A Review on Human Pose Estimation
Pose can be defined as the arrangement of human joints in a specific manner.
Interpreting Face Inference Models using Hierarchical Network Dissection
Our pipeline is inspired by Network Dissection, a popular interpretability model for object-centric and scene-centric models.
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion
In this paper, we propose a Bayesian switching dynamical model for segmentation of 3D pose data over time that uncovers interpretable patterns in the data and is generative.
Heuristic Weakly Supervised 3D Human Pose Estimation
However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their target domains.
Ranked #26 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data
In this paper, we focus on alleviating the negative effect of domain shift in both appearance and pose space for 3D human pose estimation by presenting our adapted human pose (AHuP) approach.
Ranked #116 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Invariant Representation Learning for Infant Pose Estimation with Small Data
However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of their poses.
Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting
We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions.
Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions
Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation.
Deep Markov Spatio-Temporal Factorization
This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal.
G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences
Our model, called generative low-dimensional background model (G-LBM) admits variational operations on the distribution of the manifold coordinates and simultaneously generates a low-rank structure of the latent manifold given the data.
Development of Use-specific High Performance Cyber-Nanomaterial Optical Detectors by Effective Choice of Machine Learning Algorithms
Due to their inherent variabilities, nanomaterial-based sensors are challenging to translate into real-world applications, where reliability/reproducibility is key. Recently we showed Bayesian inference can be employed on engineered variability in layered nanomaterial-based optical transmission filters to determine optical wavelengths with high accuracy/precision. In many practical applications the sensing cost/speed and long-term reliability can be equal or more important considerations. Though various machine learning tools are frequently used on sensor/detector networks to address these, nonetheless their effectiveness on nanomaterial-based sensors has not been explored. Here we show the best choice of ML algorithm in a cyber-nanomaterial detector is mainly determined by specific use considerations, e. g., accuracy, computational cost, speed, and resilience against drifts/ageing effects. When sufficient data/computing resources are provided, highest sensing accuracy can be achieved by the kNN and Bayesian inference algorithms, but but can be computationally expensive for real-time applications. In contrast, artificial neural networks are computationally expensive to train, but provide the fastest result under testing conditions and remain reasonably accurate. When data is limited, SVMs perform well even with small training sets, while other algorithms show considerable reduction in accuracy if data is scarce, hence, setting a lower limit on the size of required training data. We show by tracking/modeling the long-term drifts of the detector performance over large (1year) period, it is possible to improve the predictive accuracy with no need for recalibration. Our research shows for the first time if the ML algorithm is chosen specific to use-case, low-cost solution-processed cyber-nanomaterial detectors can be practically implemented under diverse operational requirements, despite their inherent variabilities.
Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video
Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions.
Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation
Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions.
Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis
We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework.
Indoor GeoNet: Weakly Supervised Hybrid Learning for Depth and Pose Estimation
Humans naturally perceive a 3D scene in front of them through accumulation of information obtained from multiple interconnected projections of the scene and by interpreting their correspondence.
Facial Expression and Peripheral Physiology Fusion to Decode Individualized Affective Experience
In this paper, we present a multimodal approach to simultaneously analyze facial movements and several peripheral physiological signals to decode individualized affective experiences under positive and negative emotional contexts, while considering their personalized resting dynamics.
A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
To evaluate the performance of our synthesized datasets in training deep learning-based models, we generated a large synthetic human pose dataset, called ScanAva using 3D scans of only 7 individuals based on our proposed augmentation approach.
Inner Space Preserving Generative Pose Machine
When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings.
The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose.
In-Bed Pose Estimation: Deep Learning with Shallow Dataset
Using the HOG rectification method, the pose estimation performance of CPM significantly improved by 26. 4% in PCK0. 1 criteria compared to the model without such rectification.
Background Subtraction via Fast Robust Matrix Completion
In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos.
Using Virtual Humans to Understand Real Ones
Human interactions are characterized by explicit as well as implicit channels of communication.
Decoding Emotional Experience through Physiological Signal Processing
Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner.
