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 141–150 of 1322 papers

Title	Date	Tasks	Status	Score
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds	May 5, 2025	Activity RecognitionMulti-Task Learning	CodeCode Available	5
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition	Dec 27, 2024	Activity RecognitionDecoder	CodeCode Available	5
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models	Feb 16, 2017	Activity RecognitionMotion Detection	CodeCode Available	5
A benchmark of data stream classification for human activity recognition on connected objects	Aug 27, 2020	Activity RecognitionClassification	CodeCode Available	5
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points	Feb 22, 2018	Action RecognitionActivity Prediction	CodeCode Available	5
Human activity recognition from skeleton poses	Aug 20, 2019	Action RecognitionActivity Recognition	CodeCode Available	5
Analysis of Hand Segmentation in the Wild	Mar 8, 2018	Action RecognitionActivity Recognition	CodeCode Available	5
Fine-grained Activity Recognition in Baseball Videos	Apr 9, 2018	Action DetectionActivity Detection	CodeCode Available	5
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport	Nov 15, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
Feature engineering workflow for activity recognition from synchronized inertial measurement units	Dec 18, 2019	Activity RecognitionBIG-bench Machine Learning	CodeCode Available	5

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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