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 521–530 of 1322 papers

Title	Date	Tasks	Status
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data	Feb 25, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Context-driven Active and Incremental Activity Recognition	Jun 7, 2019	Active LearningActivity Recognition	—Unverified
Context-Aware Query Selection for Active Learning in Event Recognition	Apr 9, 2019	Active LearningActivity Recognition	—Unverified
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition	Oct 25, 2018	Activity RecognitionClassification	—Unverified
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition	Dec 5, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps	Nov 17, 2021	Activity RecognitionData Augmentation	—Unverified
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection	May 11, 2019	Action DetectionActivity Detection	—Unverified
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone	May 29, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Action Recognition based Industrial Safety Violation Detection	Dec 7, 2024	Action RecognitionActivity Recognition	—Unverified
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors	Mar 4, 2020	Activity RecognitionDiversity	—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