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 851–900 of 1322 papers

TitleStatusHype
Skeleton-based Relational Reasoning for Group Activity Analysis—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition—0
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous DataCode0
Semi-supervised Federated Learning for Activity Recognition—0
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion—0
Pose And Joint-Aware Action RecognitionCode0
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Semi-supervised sequence classification through change point detection—0
Stacked Generalization for Human Activity Recognition—0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Personalization in Human Activity Recognition—0
A benchmark of data stream classification for human activity recognition on connected objectsCode0
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks—0
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System—0
AssembleNet++: Assembling Modality Representations via Attention ConnectionsCode0
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks—0
A Novel Indoor Positioning System for unprepared firefighting scenarios—0
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm—0
Towards Deep Clustering of Human Activities from Wearables—0
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition—0
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition—0
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -—0
Directional Temporal Modeling for Action Recognition—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
Social Adaptive Module for Weakly-supervised Group Activity Recognition—0
Spectrum-Guided Adversarial Disparity LearningCode0
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors—0
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos—0
Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing—0
Transfer Learning for Activity Recognition in Mobile HealthCode0
An Efficient Data Imputation Technique for Human Activity Recognition—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—0
ARC-Net: Activity Recognition Through Capsules—0
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
Background Knowledge Injection for Interpretable Sequence Classification—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices—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