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 321–330 of 1322 papers

Title	Date	Tasks	Status	Score
Deep Learning for Sensor-based Activity Recognition: A Survey	Jul 12, 2017	Activity RecognitionDeep Learning	CodeCode Available	5
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks	Jun 5, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition	Mar 8, 2022	Activity RecognitionEnsemble Learning	CodeCode Available	5
KU-HAR: An open dataset for heterogeneous human activity recognition	Mar 17, 2021	Activity RecognitionClassification	CodeCode Available	5
Large-scale weakly-supervised pre-training for video action recognition	May 2, 2019	Action ClassificationAction Recognition	CodeCode Available	5
Large Transformers are Better EEG Learners	Aug 20, 2023	Activity RecognitionEEG	CodeCode Available	5
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks	Jan 4, 2021	Activity RecognitionAnomaly Detection	—Unverified	0
Cheating off your neighbors: Improving activity recognition through corroboration	May 27, 2023	Activity RecognitionHuman Activity Recognition	—Unverified	0
Anomaly detection and regime searching in fitness-tracker data	Jan 1, 2021	Activity RecognitionAnomaly Detection	—Unverified	0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models	Jan 8, 2019	Activity RecognitionGaussian Processes	—Unverified	0

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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