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 911–920 of 1322 papers

Title	Date	Tasks	Status	Hype
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models	Mar 31, 2020	Activity Recognition	—Unverified	0
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications	Mar 30, 2020	Activity RecognitionHeart rate estimation	—Unverified	0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition	Mar 29, 2020	Active LearningActivity Recognition	CodeCode Available	0
Actor-Transformers for Group Activity Recognition	Mar 28, 2020	Activity RecognitionGroup Activity Recognition	—Unverified	0
Simultaneous Learning from Human Pose and Object Cues for Real-Time Activity Recognition	Mar 26, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks	Mar 23, 2020	Activity RecognitionAttribute	CodeCode Available	0
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits	Mar 19, 2020	Activity RecognitionTAG	CodeCode Available	0
Human Activity Recognition from Wearable Sensor Data Using Self-Attention	Mar 17, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Adversarial Transferability in Wearable Sensor Systems	Mar 17, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition	Mar 16, 2020	Activity RecognitionDiversity	—Unverified	0

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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