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 776–800 of 1322 papers

TitleStatusHype
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