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 921–930 of 1322 papers

Title	Date	Tasks	Status	Hype
Gimme Signals: Discriminative signal encoding for multimodal activity recognition	Mar 13, 2020	Action RecognitionActivity Recognition	CodeCode Available	1
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning	Mar 13, 2020	Activity RecognitionCrowd Counting	—Unverified	0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection	Mar 11, 2020	Activity RecognitionData Augmentation	—Unverified	0
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention	Mar 10, 2020	Activity RecognitionAutonomous Vehicles	—Unverified	0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals	Mar 10, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Cross-modal Learning for Multi-modal Video Categorization	Mar 7, 2020	Activity Recognitionobject-detection	—Unverified	0
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors	Mar 4, 2020	Activity RecognitionDiversity	—Unverified	0
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery	Mar 3, 2020	Activity RecognitionClustering	—Unverified	0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework	Feb 25, 2020	Activity RecognitionEdge-computing	—Unverified	0
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance	Feb 23, 2020	Activity RecognitionClassification	—Unverified	0

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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