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 451–500 of 1322 papers

TitleStatusHype
Sensing with OFDM Waveform at mmWave Band based on Micro-Doppler Analysis—0
Hard Regularization to Prevent Deep Online Clustering Collapse without Data AugmentationCode0
Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human ActivitiesCode1
Provable Robustness for Streaming Models with a Sliding Window—0
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)Code1
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling—0
Learning and Verification of Task Structure in Instructional Videos—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Dual-path Adaptation from Image to Video TransformersCode1
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles—0
Activity Recognition From Newborn Resuscitation Videos—0
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball ImageCode0
Zone-based Federated Learning for Mobile Sensing Data—0
Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning—0
Robust Multimodal Fusion for Human Activity Recognition—0
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity RecognitionCode0
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild—0
EdgeServe: A Streaming System for Decentralized Model Serving—0
Towards Activated Muscle Group Estimation in the WildCode1
Knowledge Augmented Relation Inference for Group Activity Recognition—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data—0
FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks—0
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition—0
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities—0
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration ApplicationsCode0
InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition—0
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
LaMPP: Language Models as Probabilistic Priors for Perception and ActionCode1
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation—0
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual LearningCode1
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Dataset Bias in Human Activity Recognition—0
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data—0
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living—0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding—0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Human Activity Recognition in an Open WorldCode0
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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