SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 744 papers

TitleStatusHype
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches—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
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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