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

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Showing 1061–1070 of 1322 papers

Title	Date	Tasks	Status
Online Learning of Weighted Relational Rules for Complex Event Recognition	Jan 23, 2019	Activity Recognition	CodeCode Available
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset	Jan 9, 2019	Activity RecognitionAutonomous Vehicles	—Unverified
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models	Jan 8, 2019	Activity RecognitionGaussian Processes	—Unverified
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras	Dec 28, 2018	Activity RecognitionPose Estimation	—Unverified
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition	Dec 26, 2018	Activity RecognitionGroup Activity Recognition	—Unverified
Multi-Level Sequence GAN for Group Activity Recognition	Dec 18, 2018	Action ClassificationActivity Prediction	—Unverified
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data	Dec 16, 2018	Activity RecognitionData Augmentation	—Unverified
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition	Dec 13, 2018	3D Action RecognitionAction Recognition	—Unverified
Tri-axial Self-Attention for Concurrent Activity Recognition	Dec 6, 2018	Action DetectionActivity Detection	—Unverified
Spatio-Temporal Action Graph Networks	Dec 4, 2018	Activity RecognitionAutonomous Driving	CodeCode Available

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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