SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–700 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
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
Human Activity Recognition using Recurrent Neural Networks—0
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition—0
Dynamic Vision Sensors for Human Activity RecognitionCode0
ReHAR: Robust and Efficient Human Activity Recognition—0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Learning Attribute Representation for Human Activity Recognition—0
Personalized Human Activity Recognition Using Convolutional Neural Networks—0
Human Activity Recognition for Mobile Robot—0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition—0
Latent hypernet: Exploring all Layers from Convolutional Neural Networks—0
Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data—0
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning—0
Kernel Cross-CorrelatorCode0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Action Classification and Highlighting in Videos—0
A Survey of Human Activity Recognition Using WiFi CSICode0
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable SensorsCode0
Early Improving Recurrent Elastic Highway Network—0
An Interactive Greedy Approach to Group Sparsity in High DimensionsCode0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels—0
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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