SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 744 papers

TitleStatusHype
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals—0
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition—0
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition—0
Optimized Gated Deep Learning Architectures for Sensor Fusion—0
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention—0
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies—0
Human activity recognition based on time series analysis using U-Net—0
Online Human Activity Recognition using Low-Power Wearable Devices—0
Deep Adaptive Temporal Pooling for Activity Recognition—0
Starting Movement Detection of Cyclists Using Smart Devices—0
Can You Spot the Semantic Predicate in this Video?—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
Deep Transfer Learning for Cross-domain Activity Recognition—0
Human Activity Recognition in RGB-D Videos by Dynamic Images—0
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Object Level Visual Reasoning in VideosCode0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
On Attention Models for Human Activity Recognition—0
Understanding and Improving Deep Neural Network for Activity RecognitionCode0
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All datasetsRHMOAD datasetPAMAP2HARHMDB51MM-FitRadar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)UCF-101

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Dual-Stream C3DAccuracy (Top-1)71.06—Unverified
2C3DAccuracy (Top-1)70.3—Unverified
3Dual-Stream ConvNetAccuracy (Top-1)62.77—Unverified
4SlowFast (101)Accuracy (Top-1)45.28—Unverified
#ModelMetricClaimedVerifiedStatus
1ESTIE + VGG16 (transfer-learning)Accuracy95.22—Unverified
2STIE + VGG16 (transfer-learning)Accuracy94.77—Unverified
3STIE + VGG16(fine-tuning)Accuracy86.81—Unverified
#ModelMetricClaimedVerifiedStatus
1AFVFAccuracy0.97—Unverified
2Selective HAR ClusteringNMI0.88—Unverified
3Unsupervised embedding learning for human activity recognition using wearable sensor dataNMI0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1LMSSAccuracy1—Unverified
2AFVFAccuracy0.99—Unverified
#ModelMetricClaimedVerifiedStatus
1Label-RankerAccuracy61.18—Unverified
#ModelMetricClaimedVerifiedStatus
1unsupervised statistical feature guided diffusion modelF1 - macro0.44—Unverified
#ModelMetricClaimedVerifiedStatus
1DIAT-RadHARNet1:1 Accuracy99.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Label-RankerAccuracy89.5—Unverified