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 751–800 of 1322 papers

TitleStatusHype
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
Out-of-Distribution Representation Learning for Time Series Classification—0
Generative AI based Secure Wireless Sensing for ISAC Networks—0
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds—0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images—0
Gesture Recognition in Robotic Surgery: a Review—0
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces—0
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes—0
Going Deeper into First-Person Activity Recognition—0
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding—0
Graph Neural Network based Child Activity Recognition—0
Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments—0
Grounding of the Functional Object-Oriented Network in Industrial Tasks—0
Group Activity Detection from Trajectory and Video Data in Soccer—0
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention—0
Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)—0
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives—0
Group Activity Recognition using Unreliable Tracked Pose—0
Group Activity Recognition via Dynamic Composition and Interaction—0
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
HAPRec: Hybrid Activity and Plan Recognizer—0
HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets—0
HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?—0
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data—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