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 476–500 of 1322 papers

TitleStatusHype
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities—0
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration ApplicationsCode0
InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition—0
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
LaMPP: Language Models as Probabilistic Priors for Perception and ActionCode1
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation—0
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual LearningCode1
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Dataset Bias in Human Activity Recognition—0
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data—0
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living—0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding—0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Human Activity Recognition in an Open WorldCode0
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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