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 971–980 of 1322 papers

Title	Date	Tasks	Status
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning	Nov 11, 2019	Activity RecognitionDeep Learning	—Unverified
Chirality Nets for Human Pose Regression	Oct 31, 2019	3D Human Pose Estimation3D Pose Estimation	CodeCode Available
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing	Oct 29, 2019	Activity RecognitionWeakly-supervised Learning	—Unverified
A systematic review of smartphone-based human activity recognition for health research	Oct 7, 2019	Activity RecognitionArticles	—Unverified
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles	Oct 1, 2019	Action RecognitionActivity Recognition	—Unverified
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference	Oct 1, 2019	Activity RecognitionBayesian Inference	—Unverified
New Convex Relaxations for MRF Inference With Unknown Graphs	Oct 1, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Toyota Smarthome: Real-World Activities of Daily Living	Oct 1, 2019	16kAction Classification	—Unverified
Generating Fair Universal Representations using Adversarial Models	Sep 27, 2019	Activity RecognitionFairness	—Unverified
Sign Language Recognition Analysis using Multimodal Data	Sep 24, 2019	Activity RecognitionHuman Activity Recognition	—Unverified

Show:10 25 50

← PrevPage 98 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