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 726–750 of 1322 papers

TitleStatusHype
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
Frugal Machine Learning—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances—0
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines—0
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition—0
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition—0
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning—0
Nuisance-Label Supervision: Robustness Improvement by Free Labels—0
Tutorial on Deep Learning for Human Activity RecognitionCode0
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
Lightweight Transformer in Federated Setting for Human Activity Recognition—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
A communication efficient distributed learning framework for smart environments—0
Incremental Learning Techniques for Online Human Activity Recognition—0
Multiscale Manifold Warping—0
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing—0
A distillation-based approach integrating continual learning and federated learning for pervasive services—0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition—0
Few Shot Activity Recognition Using Variational Inference—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