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 651–700 of 1322 papers

TitleStatusHype
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition—0
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model—0
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects—0
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning—0
Physical Activity Recognition by Utilising Smartphone Sensor Signals—0
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data—0
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition—0
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian MutationCode0
An adaptable cognitive microcontroller node for fitness activity recognition—0
Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition—0
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals—0
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart InfrastructureCode0
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor StreamsCode0
SSDL: Self-Supervised Dictionary Learning—0
PartImageNet: A Large, High-Quality Dataset of PartsCode1
Weakly-guided Self-supervised Pretraining for Temporal Activity DetectionCode0
Scene Graph Generation with Geometric Context—0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature—0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar SignalsCode1
Frugal Machine Learning—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances—0
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity RecognitionCode1
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines—0
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition—0
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition—0
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning—0
Nuisance-Label Supervision: Robustness Improvement by Free Labels—0
Tutorial on Deep Learning for Human Activity RecognitionCode0
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables—0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based SensorsCode1
Lightweight Transformer in Federated Setting for Human Activity Recognition—0
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—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