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 891–900 of 1322 papers

Title	Date	Tasks	Status
Language-centered Human Activity Recognition	Sep 12, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance	Jun 13, 2016	Activity RecognitionClustering	—Unverified
Large Language Models are Few-Shot Health Learners	May 24, 2023	Activity RecognitionLanguage Modeling	—Unverified
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research	Jun 9, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition	Oct 13, 2024	Activity RecognitionFew-Shot Learning	—Unverified
Latent Embeddings for Collective Activity Recognition	Sep 20, 2017	Activity Recognition	—Unverified
Latent Hierarchical Model for Activity Recognition	Mar 6, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Latent hypernet: Exploring all Layers from Convolutional Neural Networks	Nov 7, 2017	Activity RecognitionAll	—Unverified
Latent Variable Algorithms for Multimodal Learning and Sensor Fusion	Apr 23, 2019	Activity RecognitionDecision Making	—Unverified
Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)	May 20, 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