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 761–770 of 1322 papers

Title	Date	Tasks	Status	Hype
Online Learning Probabilistic Event Calculus Theories in Answer Set Programming	Mar 31, 2021	Activity RecognitionManagement	CodeCode Available	0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques	Mar 30, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence	Mar 29, 2021	Activity RecognitionFeature Engineering	—Unverified	0
MIcro-Surgical Anastomose Workflow recognition challenge report	Mar 24, 2021	Activity RecognitionManagement	—Unverified	0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors	Mar 22, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare	Mar 18, 2021	Activity RecognitionTexture Classification	—Unverified	0
KU-HAR: An open dataset for heterogeneous human activity recognition	Mar 17, 2021	Activity RecognitionClassification	CodeCode Available	0
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points	Mar 17, 2021	Activity RecognitionBenchmarking	CodeCode Available	1
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones	Mar 16, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
BASAR:Black-box Attack on Skeletal Action Recognition	Mar 9, 2021	Action RecognitionActivity Recognition	CodeCode Available	1

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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