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 501–525 of 1322 papers

TitleStatusHype
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Feature Learning for Interaction Activity Recognition in RGBD Videos—0
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Cheating off your neighbors: Improving activity recognition through corroboration—0
Federated Continual Learning through distillation in pervasive computing—0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain—0
Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices—0
Federated Multi-task Hierarchical Attention Model for Sensor Analytics—0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR—0
Federated Split Learning for Human Activity Recognition with Differential Privacy—0
FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires—0
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis—0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis—0
A distillation-based approach integrating continual learning and federated learning for pervasive services—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognition—0
Human Activity Recognition Using Visual Object Detection—0
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data—0
Context-driven Active and Incremental Activity Recognition—0
Context-Aware Query Selection for Active Learning in Event Recognition—0
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition—0
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition—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