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 981–990 of 1322 papers

Title	Date	Tasks	Status
Multi-task Self-Supervised Learning for Human Activity Detection	Jul 27, 2019	Action DetectionActivity Detection	—Unverified
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures	Feb 24, 2021	Activity Recognition	—Unverified
Multi-Type Activity Recognition in Robot-Centric Scenarios	Jul 9, 2015	Activity RecognitionVocal Bursts Type Prediction	—Unverified
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition	Dec 21, 2015	Activity RecognitionDynamic Time Warping	—Unverified
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition	Jan 30, 2023	Activity RecognitionAutonomous Driving	—Unverified
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition	Feb 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning	Aug 8, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion	Feb 22, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation	Nov 14, 2023	Action RecognitionActivity Recognition	—Unverified
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices	Jul 4, 2024	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