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 1091–1100 of 1322 papers

Title	Date	Tasks	Status
Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors	May 27, 2025	Activity RecognitionPrediction	—Unverified
Recognize Human Activities from Partially Observed Videos	Jun 1, 2013	Activity PredictionActivity Recognition	—Unverified
Recognizing Activities of Daily Living from Egocentric Images	Apr 13, 2017	Activity RecognitionGeneral Classification	—Unverified
Recognizing Activities via Bag of Words for Attribute Dynamics	Jun 1, 2013	Activity RecognitionAttribute	—Unverified
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data	Feb 23, 2015	Activity Recognition	—Unverified
Recognizing Plans by Learning Embeddings from Observed Action Distributions	Dec 5, 2017	Activity RecognitionWord Embeddings	—Unverified
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

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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