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
Activity Recognition From Newborn Resuscitation Videos—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition—0
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition—0
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition—0
Domain Generalization for Activity Recognition via Adaptive Feature Fusion—0
Domain-Adversarial Anatomical Graph Networks for Cross-User Human 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
AVD: Adversarial Video Distillation—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
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—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
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
Show:102550
← PrevPage 17 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