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 561–570 of 1322 papers

Title	Date	Tasks	Status
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors	Jul 14, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
An Empirical Study on Activity Recognition in Long Surgical Videos	May 5, 2022	Action DetectionActivity Detection	—Unverified
Deep Transfer Learning for Cross-domain Activity Recognition	Jul 20, 2018	Activity RecognitionCross-Domain Activity Recognition	—Unverified
Deep Structured Models For Group Activity Recognition	Jun 12, 2015	Activity RecognitionGroup Activity Recognition	—Unverified
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition	Nov 5, 2020	Activity RecognitionTime Series	—Unverified
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos	Apr 10, 2024	Activity RecognitionGaze Prediction	—Unverified
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos	Feb 21, 2015	Activity RecognitionGroup Activity Recognition	—Unverified
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput	Nov 11, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Positive Unlabeled Learning with a Sequential Bias	Jan 1, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features	Nov 22, 2019	Activity Recognitionintent-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