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 531–540 of 1322 papers

Title	Date	Tasks	Status
A Logic Programming Approach to Activity Recognition	May 28, 2009	Activity Recognition	—Unverified
Differentially Private 2D Human Pose Estimation	Apr 14, 2025	2D Human Pose EstimationActivity Recognition	—Unverified
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition	Sep 15, 2022	Action RecognitionActivity Recognition	—Unverified
Different Approaches for Human Activity Recognition: A Survey	Jun 11, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition	Feb 1, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning	Nov 11, 2019	Activity RecognitionDeep Learning	—Unverified
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition	Mar 22, 2025	Activity RecognitionDomain Generalization	—Unverified
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition	Jul 31, 2018	Action DetectionActivity Detection	—Unverified
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification	Apr 25, 2016	Activity RecognitionAttribute	—Unverified
Device-Free Human State Estimation using UWB Multi-Static Radios	Dec 26, 2023	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