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 1101–1110 of 1322 papers

Title	Date	Tasks	Status
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition	Jul 8, 2017	Activity RecognitionEnsemble Learning	—Unverified
Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey	Apr 17, 2023	Activity RecognitionDeep Learning	—Unverified
Applications of human activity recognition in industrial processes -- Synergy of human and technology	Dec 5, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Approaches and Applications of Early Classification of Time Series: A Review	May 6, 2020	Activity RecognitionClassification	—Unverified
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition	Oct 25, 2018	Activity RecognitionClassification	—Unverified
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data	Feb 25, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognition	May 28, 2025	Activity RecognitionEgocentric Activity Recognition	—Unverified
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation	Feb 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
ARC-Net: Activity Recognition Through Capsules	Jul 6, 2020	Activity RecognitionARC	—Unverified
Are Accelerometers for Activity Recognition a Dead-end?	Jan 22, 2020	Activity RecognitionFeature Engineering	—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