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 941–950 of 1322 papers

Title	Date	Tasks	Status
Human Activity Recognition using Multi-Head CNN followed by LSTM	Feb 21, 2020	Activity RecognitionBIG-bench Machine Learning	CodeCode Available
Three-Stream Fusion Network for First-Person Interaction Recognition	Feb 19, 2020	Activity RecognitionHuman Interaction Recognition	—Unverified
Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection	Feb 8, 2020	Action DetectionActivity Recognition	CodeCode Available
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos	Feb 6, 2020	Action RecognitionAction Recognition In Videos	—Unverified
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront	Feb 4, 2020	Activity RecognitionPedestrian Detection	—Unverified
Multi-label Prediction in Time Series Data using Deep Neural Networks	Jan 27, 2020	Activity RecognitionEvent Detection	—Unverified
Zero-Shot Activity Recognition with Videos	Jan 22, 2020	Action RecognitionActivity Recognition	—Unverified
Are Accelerometers for Activity Recognition a Dead-end?	Jan 22, 2020	Activity RecognitionFeature Engineering	—Unverified
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities	Jan 21, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures	Jan 19, 2020	Activity RecognitionDiversity	—Unverified

Show:10 25 50

← PrevPage 95 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