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

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Showing 101–125 of 1322 papers

TitleStatusHype
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning StudyCode1
Online Semi-Supervised Learning of Composite Event Rules by Combining Structure and Mass-Based Predicate SimilarityCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Panoramic Human Activity RecognitionCode1
Privacy and Utility Preserving Sensor-Data TransformationsCode1
Protecting Sensory Data against Sensitive InferencesCode1
Real-world Anomaly Detection in Surveillance VideosCode1
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable DataCode1
Convolutional Tensor-Train LSTM for Spatio-temporal LearningCode1
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled DataCode1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity ParsingCode1
SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball TacticCode1
Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic GraphCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Spatio-Temporal Dynamic Inference Network for Group Activity RecognitionCode1
TASAR: Transfer-based Attack on Skeletal Action RecognitionCode1
Temporal Action Localization for Inertial-based Human Activity RecognitionCode1
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual LearningCode1
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature CalibrationCode1
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation LearningCode1
A Review of Deep Learning Methods for Photoplethysmography DataCode1
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
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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