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 601–650 of 1322 papers

TitleStatusHype
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
Decoding Children's Social Behavior—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning—0
Dataset Bias in Human Activity Recognition—0
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting—0
Integrated Human Activity Sensing and Communications—0
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare—0
Assessing the State of Self-Supervised Human Activity Recognition using Wearables—0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning—0
Human Activity Recognition Models in Ontology Networks—0
Human Activity Recognition in RGB-D Videos by Dynamic Images—0
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study—0
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
A communication efficient distributed learning framework for smart environments—0
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Human Activity Recognition for Mobile Robot—0
Human Activity Recognition for Edge Devices—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Cross-user activity recognition via temporal relation optimal transport—0
Human activity recognition based on time series analysis using U-Net—0
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques—0
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Human Action Attribute Learning From Video Data Using Low-Rank Representations—0
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—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