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 401–425 of 1322 papers

TitleStatusHype
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Convolutional Relational Machine for Group Activity Recognition—0
Domain Generalization for Activity Recognition via Adaptive Feature Fusion—0
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition—0
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition—0
Don't freeze: Finetune encoders for better Self-Supervised HAR—0
DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition—0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles—0
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System—0
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition—0
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network—0
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
Asymmetric Residual Neural Network for Accurate Human Activity Recognition—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
Are Accelerometers for Activity Recognition a Dead-end?—0
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning—0
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—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