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 841–850 of 1322 papers

Title	Date	Tasks	Status
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition	Sep 18, 2019	Activity RecognitionCaption Generation	—Unverified
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues	Dec 2, 2019	3D Action RecognitionAction Recognition	—Unverified
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches	Nov 1, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Skeleton-based Relational Reasoning for Group Activity Analysis	Nov 11, 2020	Activity RecognitionGroup Activity Recognition	—Unverified
Skeleton Focused Human Activity Recognition in RGB Video	Apr 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
SkeleTR: Towards Skeleton-based Action Recognition in the Wild	Jan 1, 2023	Action ClassificationAction Detection	—Unverified
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild	Sep 20, 2023	Action ClassificationAction Detection	—Unverified
SkipW: Resource adaptable RNN with strict upper computational limit	Jan 1, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data	Jan 17, 2023	Activity Recognition	—Unverified
Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning	Mar 8, 2023	Activity RecognitionEnsemble Learning	—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