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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 581–590 of 1322 papers

Title	Date	Tasks	Status
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes	Feb 25, 2025	Activity RecognitionExplainable artificial intelligence	—Unverified
Going Deeper into First-Person Activity Recognition	May 12, 2016	Action RecognitionActivity Recognition	—Unverified
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding	Jun 14, 2024	Activity RecognitionMMR total	—Unverified
Graph Neural Network based Child Activity Recognition	Dec 18, 2022	Activity RecognitionGraph Neural Network	—Unverified
Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments	Sep 11, 2023	Activity RecognitionBayesian Optimization	—Unverified
Grounding of the Functional Object-Oriented Network in Industrial Tasks	Apr 5, 2022	Activity Recognition	—Unverified
Group Activity Detection from Trajectory and Video Data in Soccer	Apr 21, 2020	Action DetectionActivity Detection	—Unverified
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention	Mar 10, 2020	Activity RecognitionAutonomous Vehicles	—Unverified
Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)	Aug 31, 2022	Activity RecognitionGroup Activity Recognition	—Unverified
Forensic Video Analytic Software	Sep 17, 2023	Activity RecognitionAnomaly Detection	—Unverified

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All datasets RWF-2000 Stanford40 First-Person Hand Action Benchmark Self-stimulatory Behavior Dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Structured Keypoint Pooling	Accuracy	93.4	—	Unverified
2	Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics dataset	Accuracy	90.4	—	Unverified
3	Human Skeletons + Change Detection	Accuracy	90.25	—	Unverified
4	Separable Convolutional LSTM	Accuracy	89.75	—	Unverified
5	SPIL Convolution	Accuracy	89.3	—	Unverified
6	Flow Gated Network	Accuracy	87.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FocusCLIP	Top-3 Accuracy (%)	10.47	—	Unverified
2	CLIP	Top-3 Accuracy (%)	6.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Boutaleb et al.	1:1 Accuracy	97.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	all-landmark-model	Activity Recognition	0.76	—	Unverified