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 801–850 of 1322 papers

TitleStatusHype
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification—0
Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification—0
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition—0
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences—0
Human Action Attribute Learning From Video Data Using Low-Rank Representations—0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques—0
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection—0
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks—0
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Human activity recognition based on time series analysis using U-Net—0
Human Activity Recognition for Edge Devices—0
Human Activity Recognition for Mobile Robot—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Human Activity Recognition in RGB-D Videos by Dynamic Images—0
Human Activity Recognition Models in Ontology Networks—0
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature—0
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data—0
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—0
Human activity recognition using improved dynamic image—0
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey—0
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture—0
Human Activity Recognition Using Multichannel Convolutional Neural Network—0
Human Activity Recognition using Recurrent Neural Networks—0
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
Human Activity Recognition using Smartphone—0
Human Activity Recognition using Smartphones—0
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects—0
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E—0
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment—0
Human Body Parts Tracking: Applications to Activity Recognition—0
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition—0
Human Interaction Recognition Framework based on Interacting Body Part Attention—0
Human-like Relational Models for Activity Recognition in Video—0
Human Pose Estimation using Motion Priors and Ensemble Models—0
Hunting Group Clues with Transformers for Social Group Activity Recognition—0
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
Identifying First-person Camera Wearers in Third-person Videos—0
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets—0
Image based Eye Gaze Tracking and its Applications—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration—0
Show:102550
← PrevPage 17 of 27Next →
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