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 871–880 of 1322 papers

Title	Date	Tasks	Status
Starting Movement Detection of Cyclists Using Smart Devices	Aug 8, 2018	Activity Recognitionfeature selection	—Unverified
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room	Dec 19, 2023	Action ClassificationActivity Recognition	—Unverified
Stratified Transfer Learning for Cross-domain Activity Recognition	Dec 25, 2017	Activity RecognitionCross-Domain Activity Recognition	—Unverified
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition	Nov 13, 2015	Activity RecognitionGroup Activity Recognition	—Unverified
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels	Jul 3, 2017	Activity RecognitionBayesian Optimization	—Unverified
STS Classification with Dual-stream CNN	May 20, 2018	Activity RecognitionClassification	—Unverified
Student Activity Recognition in Classroom Environments using Transfer Learning	Dec 1, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Super Normal Vector for Activity Recognition Using Depth Sequences	Jun 1, 2014	Activity RecognitionGeneral Classification	—Unverified
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning	Jun 1, 2018	Activity Recognition	—Unverified
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery	Mar 12, 2025	Activity RecognitionAnatomy	—Unverified

Show:10 25 50

← PrevPage 88 of 133Next →

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