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 301–325 of 1322 papers

TitleStatusHype
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
Multi-objective Feature Selection in Remote Health Monitoring Applications—0
TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition—0
Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors—0
Group Activity Recognition using Unreliable Tracked Pose—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Bi-Causal: Group Activity Recognition via Bidirectional Causality—0
Directional Antenna Systems for Long-Range Through-Wall Human Activity RecognitionCode0
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity RecognitionCode0
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing—0
Device-Free Human State Estimation using UWB Multi-Static Radios—0
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses—0
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass SurgeryCode1
Online Semi-Supervised Learning of Composite Event Rules by Combining Structure and Mass-Based Predicate SimilarityCode1
Enhanced Spatio- Temporal Image Encoding for Online Human Activity RecognitionCode0
Multi-stage Learning for Radar Pulse Activity SegmentationCode1
Deep Unsupervised Domain Adaptation for Time Series Classification: a BenchmarkCode1
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
Towards a geometric understanding of Spatio Temporal Graph Convolution NetworksCode0
Navigating Open Set Scenarios for Skeleton-based Action RecognitionCode1
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
Show:102550
← PrevPage 13 of 53Next →
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