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 1031–1040 of 1322 papers

Title	Date	Tasks	Status
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges	Nov 14, 2023	Activity Recognition	—Unverified
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task	Nov 11, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition	Apr 30, 2020	Activity Recognition	—Unverified
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion	Nov 1, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Performance of different machine learning methods on activity recognition and pose estimation datasets	Oct 19, 2022	Activity RecognitionPose Estimation	—Unverified
Personalization in Human Activity Recognition	Sep 1, 2020	Activity RecognitionDiversity	—Unverified
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework	Feb 25, 2020	Activity RecognitionEdge-computing	—Unverified
Personalized Human Activity Recognition Using Convolutional Neural Networks	Jan 25, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Personalized Semi-Supervised Federated Learning for Human Activity Recognition	Apr 15, 2021	Active LearningActivity Recognition	—Unverified
Personalizing human activity recognition models using incremental learning	May 29, 2019	Activity RecognitionGeneral Classification	—Unverified

Show:10 25 50

← PrevPage 104 of 133Next →

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