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 701–725 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
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious 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
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
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
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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