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 501–525 of 1322 papers

TitleStatusHype
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Large Language Models are Few-Shot Health Learners—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial SensorsCode0
ConvBoost: Boosting ConvNets for Sensor-based Activity RecognitionCode0
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—0
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal—0
Privacy in Multimodal Federated Human Activity Recognition—0
Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition—0
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition—0
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable SensorsCode0
Is end-to-end learning enough for fitness activity recognition?—0
Group Activity Recognition via Dynamic Composition and Interaction—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
RHM: Robot House Multi-view Human Activity Recognition DatasetCode0
A Survey on Multi-Resident Activity Recognition in Smart Environments—0
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning—0
Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold ProcessorsCode0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—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