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

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Showing 761–770 of 1322 papers

Title	Date	Tasks	Status
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping	Jul 17, 2021	Action RecognitionActivity Recognition	CodeCode Available
Human-like Relational Models for Activity Recognition in Video	Jul 12, 2021	Activity Recognition	—Unverified
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation	Jul 6, 2021	Activity RecognitionClassification	—Unverified
Reducing numerical precision preserves classification accuracy in Mondrian Forests	Jun 28, 2021	Activity RecognitionClassification	CodeCode Available
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers	Jun 28, 2021	Activity RecognitionFeature Engineering	—Unverified
Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks	Jun 23, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology	Jun 22, 2021	Activity RecognitionClustering	—Unverified
A Survey on Human-aware Robot Navigation	Jun 22, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
A compressive multi-kernel method for privacy-preserving machine learning	Jun 20, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification	Jun 17, 2021	Activity RecognitionClassification	—Unverified

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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