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 1231–1240 of 1322 papers

Title	Date	Tasks	Status
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator	Feb 21, 2022	Activity RecognitionData Augmentation	—Unverified
Cross-Country Skiing Gears Classification using Deep Learning	Jun 27, 2017	Activity RecognitionClassification	—Unverified
Cross-domain Activity Recognition via Substructural Optimal Transport	Jan 29, 2021	Activity RecognitionClustering	—Unverified
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition	Oct 22, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Cross-modal Learning for Multi-modal Video Categorization	Mar 7, 2020	Activity Recognitionobject-detection	—Unverified
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space	Jan 1, 2023	Activity RecognitionClustering	—Unverified
Cross-position Activity Recognition with Stratified Transfer Learning	Jun 26, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks	Mar 29, 2019	Activity RecognitionGenerative Adversarial Network	—Unverified
Cross-user activity recognition using deep domain adaptation with temporal relation information	Mar 12, 2024	Activity RecognitionDomain Adaptation	—Unverified
Cross-user activity recognition via temporal relation optimal transport	Mar 12, 2024	Activity RecognitionDomain Adaptation	—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