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 571–580 of 1322 papers

Title	Date	Tasks	Status
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors	Mar 4, 2020	Activity RecognitionDiversity	—Unverified
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition	Apr 10, 2024	Activity RecognitionMulti-Label Classification	—Unverified
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition	Nov 11, 2020	Activity RecognitionDomain Adaptation	—Unverified
Action Recognition based Industrial Safety Violation Detection	Dec 7, 2024	Action RecognitionActivity Recognition	—Unverified
Appearance Based Robot and Human Activity Recognition System	Feb 4, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images	Jul 8, 2024	Activity Recognitionimage-classification	—Unverified
Gesture Recognition in Robotic Surgery: a Review	Jan 29, 2021	Activity RecognitionArticles	—Unverified
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces	Jun 28, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint	Dec 31, 2013	Activity Recognitionfeature selection	—Unverified
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario	Jun 18, 2020	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