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 651–675 of 1322 papers

TitleStatusHype
Fine-grained Activities of People Worldwide—0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
WiFi-based Spatiotemporal Human Action Perception—0
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
ProActive: Self-Attentive Temporal Point Process Flows for Activity SequencesCode0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens—0
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
Benchmark of DNN Model Search at Deployment Time—0
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V ProcessorsCode0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes—0
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems—0
Koopman pose predictions for temporally consistent human walking estimations—0
An Empirical Study on Activity Recognition in Long Surgical Videos—0
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition—0
A Close Look into Human Activity Recognition Models using Deep Learning—0
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings—0
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptationCode0
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates—0
EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression—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