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 376–400 of 1322 papers

TitleStatusHype
HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?—0
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition—0
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR—0
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition—0
The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric VideoCode0
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization—0
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted SurgeryCode0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—0
Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices—0
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network—0
Phase-driven Domain Generalizable Learning for Nonstationary Time Series—0
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures—0
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare—0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
mmID: High-Resolution mmWave Imaging for Human Identification—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data—0
Sensor-Based Data Acquisition via Ubiquitous Device to Detect Muscle Strength Training Activities—0
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity RecognitionCode0
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review—0
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern RecognitionCode0
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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