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 576–600 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
From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—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
Contrastive Learning for Time Series on Dynamic Graphs—0
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation—0
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers—0
Group Activity Recognition via Dynamic Composition and Interaction—0
Compact CNN for Indexing Egocentric Videos—0
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables—0
Contrastive Predictive Coding for Human Activity Recognition—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm—0
Appearance Based Robot and Human Activity Recognition System—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