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 651–660 of 1322 papers

Title	Date	Tasks	Status
Human activity recognition using improved dynamic image	Nov 15, 2020	Action RecognitionActivity Recognition	—Unverified
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey	Apr 19, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture	May 2, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition Using Multichannel Convolutional Neural Network	Jan 17, 2021	Activity RecognitionGeneral Classification	—Unverified
Hunting Group Clues with Transformers for Social Group Activity Recognition	Jul 12, 2022	Activity RecognitionGroup Activity Recognition	—Unverified
Human Activity Recognition using Recurrent Neural Networks	Apr 19, 2018	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks	Mar 11, 2024	Activity RecognitionAge Classification	—Unverified
FMCW Radar Principles and Human Activity Recognition Systems: Foundations, Techniques, and Applications	Oct 11, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition Using Self-Supervised Representations of Wearable Data	Apr 26, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos	Oct 16, 2018	Activity RecognitionActivity Recognition In Videos	—Unverified

Show:10 25 50

← PrevPage 66 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