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

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Showing 611–620 of 1322 papers

Title	Date	Tasks	Status
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model	Jun 1, 2024	Action RecognitionActivity Recognition	—Unverified
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data	Nov 8, 2022	Activity Recognition	—Unverified
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition	Sep 26, 2024	Activity RecognitionGraph Representation Learning	—Unverified
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification	Sep 22, 2022	Activity RecognitionClassification	—Unverified
Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification	Nov 27, 2024	Activity RecognitionClassification	—Unverified
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection	May 11, 2019	Action DetectionActivity Detection	—Unverified
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition	Oct 22, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies	Sep 21, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images	Oct 16, 2024	Activity RecognitionContinual Learning	—Unverified
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression	Dec 25, 2013	Activity RecognitionGeneral 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