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 601–625 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
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
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—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
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Cross-user activity recognition via temporal relation optimal transport—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—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