SOTAVerified

Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 1322 papers

TitleStatusHype
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity RecognitionCode0
Temporal Action Segmentation with High-level Complex Activity Labels—0
AdaRNN: Adaptive Learning and Forecasting of Time SeriesCode0
Pose is all you need: The pose only group activity recognition system (POGARS)—0
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System—0
Neural Style Transfer Enhanced Training Support For Human Activity Recognition—0
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition—0
Inference for Change Points in High Dimensional Mean Shift Models—0
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People GroupingCode0
Human-like Relational Models for Activity Recognition in Video—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Reducing numerical precision preserves classification accuracy in Mondrian ForestsCode0
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers—0
Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural NetworksCode0
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology—0
A Survey on Human-aware Robot Navigation—0
A compressive multi-kernel method for privacy-preserving machine learning—0
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification—0
Privacy-Preserving Eye-tracking Using Deep Learning—0
Long Term Object Detection and Tracking in Collaborative Learning Environments—0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
Similarity Embedding Networks for Robust Human Activity Recognition—0
Quantization and Deployment of Deep Neural Networks on MicrocontrollersCode0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified