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 771–780 of 1322 papers

Title	Date	Tasks	Status
Recurrent Modeling of Interaction Context for Collective Activity Recognition	Jul 1, 2017	Activity RecognitionDescriptive	—Unverified
Redundant feature screening method for human activity recognition based on attention purification mechanism	Mar 30, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
ReHAR: Robust and Efficient Human Activity Recognition	Feb 27, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Relevance Topic Model for Unstructured Social Group Activity Recognition	Dec 1, 2013	Activity RecognitionAttribute	—Unverified
Reshaping Visual Datasets for Domain Adaptation	Dec 1, 2013	Activity RecognitionDomain Adaptation	—Unverified
Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs	Jul 1, 2017	Activity RecognitionGesture Recognition	—Unverified
Resource-Efficient Computing in Wearable Systems	Jul 7, 2019	Activity RecognitionGeneral Classification	—Unverified
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification	Jul 7, 2019	Activity RecognitionBIG-bench Machine Learning	—Unverified
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition	Apr 30, 2022	Activity RecognitionContinual Learning	—Unverified
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging	Jul 1, 2017	Activity Recognitionregression	—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