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

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Showing 951–1000 of 1322 papers

TitleStatusHype
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance—0
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze—0
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition—0
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition—0
Human Gaze Guided Attention for Surgical Activity Recognition—0
Utility-aware Privacy-preserving Data Releasing—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild—0
VCHAR:Variance-Driven Complex Human Activity Recognition framework with Generative Representation—0
VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration—0
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition—0
VicTR: Video-conditioned Text Representations for Activity Recognition—0
Video2IMU: Realistic IMU features and signals from videos—0
Video-based Exercise Classification and Activated Muscle Group Prediction with Hybrid X3D-SlowFast Network—0
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model—0
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
Vision-Based Activity Recognition in Children with Autism-Related Behaviors—0
Visually Guided Spatial Relation Extraction from Text—0
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel—0
Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM Agents—0
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos—0
Wallcamera: Reinventing the Wheel?—0
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data—0
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables—0
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition—0
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation—0
Wearable-based behaviour interpolation for semi-supervised human activity recognition—0
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach—0
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians—0
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition—0
WiFi-based Spatiotemporal Human Action Perception—0
Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition—0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals—0
Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field—0
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values—0
X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing—0
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos—0
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers—0
Zero-Shot Activity Recognition with Videos—0
DASZL: Dynamic Action Signatures for Zero-shot Learning—0
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs—0
Smart Laptop Bag with Machine Learning for Activity Recognition—0
Zone-based Federated Learning for Mobile Sensing Data—0
Human Activity Recognition Using Visual Object Detection—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing—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