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 401–425 of 1322 papers

TitleStatusHype
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series PredictionCode1
Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic VideosCode1
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems—0
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation LearningCode1
Robust Explainer Recommendation for Time Series ClassificationCode0
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition—0
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition—0
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition—0
Cheating off your neighbors: Improving activity recognition through corroboration—0
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
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
Large Language Models are Few-Shot Health Learners—0
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
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial SensorsCode0
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—0
Human skeletons and change detection for efficient violence detection in surveillance videosCode1
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
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
Show:102550
← PrevPage 17 of 53Next →
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