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 961–970 of 1322 papers

Title	Date	Tasks	Status	Hype
RWF-2000: An Open Large Scale Video Database for Violence Detection	Nov 14, 2019	Action ClassificationAction Recognition	CodeCode Available	0
Privacy and Utility Preserving Sensor-Data Transformations	Nov 14, 2019	Activity Recognition	CodeCode Available	1
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning	Nov 11, 2019	Activity RecognitionDeep Learning	—Unverified	0
Chirality Nets for Human Pose Regression	Oct 31, 2019	3D Human Pose Estimation3D Pose Estimation	CodeCode Available	0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing	Oct 29, 2019	Activity RecognitionWeakly-supervised Learning	—Unverified	0
A systematic review of smartphone-based human activity recognition for health research	Oct 7, 2019	Activity RecognitionArticles	—Unverified	0
New Convex Relaxations for MRF Inference With Unknown Graphs	Oct 1, 2019	Activity RecognitionHuman Activity Recognition	—Unverified	0
Toyota Smarthome: Real-World Activities of Daily Living	Oct 1, 2019	16kAction Classification	—Unverified	0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference	Oct 1, 2019	Activity RecognitionBayesian Inference	—Unverified	0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles	Oct 1, 2019	Action RecognitionActivity Recognition	—Unverified	0

Show:10 25 50

← PrevPage 97 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