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 681–690 of 1322 papers

Title	Date	Tasks	Status
Seeker: Synergizing Mobile and Energy Harvesting Wearable Sensors for Human Activity Recognition	Mar 25, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing	Mar 25, 2022	Activity RecognitionEnsemble Learning	—Unverified
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition	Mar 23, 2022	Activity RecognitionClustering	—Unverified
FAR: Fourier Aerial Video Recognition	Mar 21, 2022	Action RecognitionActivity Recognition	CodeCode Available
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions	Mar 14, 2022	Activity RecognitionContinual Learning	—Unverified
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition	Mar 14, 2022	Activity RecognitionFew-Shot Learning	—Unverified
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks	Mar 11, 2022	Activity Recognitionclass-incremental learning	—Unverified
Defending Black-box Skeleton-based Human Activity Classifiers	Mar 9, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
Human Gaze Guided Attention for Surgical Activity Recognition	Mar 9, 2022	Activity RecognitionVideo Understanding	—Unverified
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition	Mar 8, 2022	Activity RecognitionEnsemble Learning	CodeCode Available

Show:10 25 50

← PrevPage 69 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