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 1061–1070 of 1322 papers

Title	Date	Tasks	Status
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
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data	Dec 3, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference	Nov 27, 2018	Activity RecognitionBayesian Inference	—Unverified
LSTA: Long Short-Term Attention for Egocentric Action Recognition	Nov 26, 2018	Action RecognitionActivity Recognition	CodeCode Available
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images	Nov 25, 2018	Action RecognitionActivity Recognition	—Unverified
Online Collective Animal Movement Activity Recognition	Nov 22, 2018	Activity Recognition	—Unverified
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification	Nov 20, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables	Nov 20, 2018	Activity RecognitionMulti-class Classification	—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