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 376–400 of 1322 papers

TitleStatusHype
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Eco-Friendly Sensing for Human Activity Recognition—0
Shuffled Differentially Private Federated Learning for Time Series Data Analytics—0
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data—0
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study—0
Siamese Networks for Weakly Supervised Human Activity Recognition—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows—0
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition—0
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning—0
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition—0
Exploring Transformers for On-Line Handwritten Signature Verification—0
Don't freeze: Finetune encoders for better Self-Supervised HAR—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion SensingCode1
M3Act: Learning from Synthetic Human Group Activities—0
MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edgeCode0
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces—0
Differentially Private Video Activity Recognition—0
A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System—0
Attention-Refined Unrolling for Sparse Sequential micro-Doppler ReconstructionCode0
Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data ScarcityCode0
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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