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 991–1000 of 1322 papers

Title	Date	Tasks	Status
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition	Mar 23, 2022	Activity RecognitionClustering	—Unverified
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis	May 21, 2024	Activity RecognitionContrastive Learning	—Unverified
Nested Motion Descriptors	Jun 1, 2015	Activity RecognitionOptical Flow Estimation	—Unverified
Neural Style Transfer Enhanced Training Support For Human Activity Recognition	Jul 27, 2021	Activity RecognitionData Augmentation	—Unverified
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition	Jun 8, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
New Convex Relaxations for MRF Inference With Unknown Graphs	Oct 1, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Non-Linear Temporal Subspace Representations for Activity Recognition	Mar 27, 2018	Action RecognitionActivity Recognition	—Unverified
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction	Aug 3, 2021	Activity RecognitionDecoder	—Unverified
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition	Sep 25, 2024	Activity RecognitionData Augmentation	—Unverified
Novel evaluation of surgical activity recognition models using task-based efficiency metrics	Jul 3, 2019	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