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 701–725 of 1322 papers

TitleStatusHype
Overview of Human Activity Recognition Using Sensor Data—0
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models—0
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer—0
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology—0
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges—0
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task—0
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Performance of different machine learning methods on activity recognition and pose estimation datasets—0
Personalization in Human Activity Recognition—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Human Activity Recognition Using Convolutional Neural Networks—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Personalizing human activity recognition models using incremental learning—0
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning—0
Phase-driven Domain Generalizable Learning for Nonstationary Time Series—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Physical Activity Recognition by Utilising Smartphone Sensor Signals—0
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings—0
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition—0
Plots Unlock Time-Series Understanding in Multimodal Models—0
Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses—0
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition—0
Pose is all you need: The pose only group activity recognition system (POGARS)—0
Poselet Key-Framing: A Model for Human Activity Recognition—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