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
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
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor DataCode1
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
SeCo: Exploring Sequence Supervision for Unsupervised Representation LearningCode1
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
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
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
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors—0
Spectrum-Guided Adversarial Disparity LearningCode0
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
ARC-Net: Activity Recognition Through Capsules—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—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
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Background Knowledge Injection for Interpretable Sequence Classification—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
On Matched Filtering for Statistical Change Point Detection—0
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition—0
Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization—0
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate—0
Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Utility-aware Privacy-preserving Data Releasing—0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors—0
Approaches and Applications of Early Classification of Time Series: A Review—0
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition—0
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWBCode1
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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