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 1201–1210 of 1322 papers

Title	Date	Tasks	Status
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition	Feb 6, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition	Jan 17, 2021	Activity Recognition	—Unverified
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR	Mar 22, 2024	Active LearningActivity Recognition	—Unverified
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field	Jan 12, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition	Feb 1, 2022	Activity RecognitionContrastive Learning	—Unverified
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos	Oct 16, 2018	Activity RecognitionActivity Recognition In Videos	—Unverified
Compact CNN for Indexing Egocentric Videos	Apr 28, 2015	Activity RecognitionOptical Flow Estimation	—Unverified
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition	Feb 28, 2024	Activity RecognitionComputational Efficiency	—Unverified
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video	Jun 1, 2014	Activity RecognitionDescriptive	—Unverified
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure	Feb 6, 2017	Activity RecognitionConcurrent 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