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 1226–1250 of 1322 papers

TitleStatusHype
Contrastive Predictive Coding for Human Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
Convolutional Relational Machine for Group Activity Recognition—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
Cross-Country Skiing Gears Classification using Deep Learning—0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Cross-modal Learning for Multi-modal Video Categorization—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
Cross-user activity recognition via temporal relation optimal transport—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare—0
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Dataset Bias in Human Activity Recognition—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Decoding Children's Social Behavior—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