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 776–800 of 1322 papers

TitleStatusHype
Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems—0
Egocentric Activity Recognition and Localization on a 3D Map—0
Explainable Activity Recognition for Smart Home Systems—0
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity RecognitionCode0
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
Human Activity Recognition Models in Ontology Networks—0
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables—0
Three-stream network for enriched Action Recognition—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis—0
Self-Supervised WiFi-Based Activity Recognition—0
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Description of Structural Biases and Associated Data in Sensor-Rich Environments—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
Selective Feature Compression for Efficient Activity Recognition Inference—0
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition—0
Online Learning Probabilistic Event Calculus Theories in Answer Set ProgrammingCode0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
MIcro-Surgical Anastomose Workflow recognition challenge report—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare—0
KU-HAR: An open dataset for heterogeneous human activity recognitionCode0
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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