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 1061–1070 of 1322 papers

Title	Date	Tasks	Status
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images	Nov 25, 2018	Action RecognitionActivity Recognition	—Unverified
Privacy-Preserving Eye-tracking Using Deep Learning	Jun 17, 2021	Activity RecognitionDeep Learning	—Unverified
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution	Apr 12, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens	Jun 8, 2022	Action RecognitionActivity Recognition	—Unverified
Probabilistic Sensor Fusion for Ambient Assisted Living	Feb 4, 2017	Activity RecognitionSensor Fusion	—Unverified
Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models	Jul 22, 2024	Action UnderstandingActivity Recognition	—Unverified
Process-aware Human Activity Recognition	Nov 13, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning	Mar 22, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Progress Estimation and Phase Detection for Sequential Processes	Feb 28, 2017	Activity RecognitionMultimodal Deep Learning	—Unverified
Progressive Relation Learning for Group Activity Recognition	Aug 8, 2019	Activity RecognitionDeep Reinforcement Learning	—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