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 1071–1080 of 1322 papers

Title	Date	Tasks	Status
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
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables	Nov 20, 2018	Activity RecognitionMulti-class Classification	—Unverified
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification	Nov 20, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing	Nov 12, 2018	Activity RecognitionGesture Recognition	—Unverified
BAR: Bayesian Activity Recognition using variational inference	Nov 8, 2018	Activity RecognitionBayesian Inference	—Unverified
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches	Nov 1, 2018	Activity RecognitionHuman Activity Recognition	—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