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 301–310 of 1322 papers

Title	Date	Tasks	Status
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition	Oct 23, 2021	Activity RecognitionDecoder	—Unverified
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition	Oct 22, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Cross-domain Activity Recognition via Substructural Optimal Transport	Jan 29, 2021	Activity RecognitionClustering	—Unverified
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies	Sep 21, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
A Close Look into Human Activity Recognition Models using Deep Learning	Apr 26, 2022	Activity RecognitionDeep Learning	—Unverified
Cross-Country Skiing Gears Classification using Deep Learning	Jun 27, 2017	Activity RecognitionClassification	—Unverified
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator	Feb 21, 2022	Activity RecognitionData Augmentation	—Unverified
A Review of Machine Learning Methods Applied to Video Analysis Systems	Dec 8, 2023	Active LearningActivity Recognition	—Unverified
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images	Oct 16, 2024	Activity RecognitionContinual Learning	—Unverified
Creating a Large-scale Synthetic Dataset for Human Activity Recognition	Jul 21, 2020	Activity RecognitionHuman Activity Recognition	—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