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 151–175 of 1322 papers

TitleStatusHype
HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional MambaCode0
Hard Regularization to Prevent Deep Online Clustering Collapse without Data AugmentationCode0
Hierarchical Attentive Recurrent TrackingCode0
Human activity recognition from skeleton posesCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People GroupingCode0
Robust Explainer Recommendation for Time Series ClassificationCode0
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity RecognitionCode0
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial SensorsCode0
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian ManifoldsCode0
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable SensorsCode0
A Comparison of Deep Learning and Established Methods for Calf Behaviour MonitoringCode0
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity RecognitionCode0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
KU-HAR: An open dataset for heterogeneous human activity recognitionCode0
Fine-grained Activity Recognition in Baseball VideosCode0
Alignment-based conformance checking over probabilistic eventsCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
FAR: Fourier Aerial Video RecognitionCode0
A Hierarchical Deep Temporal Model for Group Activity RecognitionCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity RecognitionCode0
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity RecognitionCode0
Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative MeasuresCode0
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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