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 321–330 of 1322 papers

Title	Date	Tasks	Status
Cross-Country Skiing Gears Classification using Deep Learning	Jun 27, 2017	Activity RecognitionClassification	—Unverified
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors	Jun 25, 2020	Activity RecognitionHard Attention	—Unverified
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator	Feb 21, 2022	Activity RecognitionData Augmentation	—Unverified
A Review of Machine Learning Methods Applied to Video Analysis Systems	Dec 8, 2023	Active LearningActivity Recognition	—Unverified
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting	Mar 28, 2025	Activity Recognition	—Unverified
Dataiku's Solution to SPHERE's Activity Recognition Challenge	Oct 10, 2016	Activity RecognitionBIG-bench Machine Learning	—Unverified
Dataset Bias in Human Activity Recognition	Jan 19, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Creating a Large-scale Synthetic Dataset for Human Activity Recognition	Jul 21, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight	Dec 5, 2022	Activity RecognitionDomain Adaptation	—Unverified
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition	Jan 3, 2024	Activity RecognitionHuman 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