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 731–740 of 1322 papers

Title	Date	Tasks	Status	Hype
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology	Jun 22, 2021	Activity RecognitionClustering	—Unverified	0
A Survey on Human-aware Robot Navigation	Jun 22, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
A compressive multi-kernel method for privacy-preserving machine learning	Jun 20, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification	Jun 17, 2021	Activity RecognitionClassification	—Unverified	0
Privacy-Preserving Eye-tracking Using Deep Learning	Jun 17, 2021	Activity RecognitionDeep Learning	—Unverified	0
Long Term Object Detection and Tracking in Collaborative Learning Environments	Jun 2, 2021	Activity RecognitionActivity Recognition In Videos	—Unverified	0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare	Jun 2, 2021	Activity RecognitionFederated Learning	—Unverified	0
Similarity Embedding Networks for Robust Human Activity Recognition	May 31, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Meta-HAR: Federated Representation Learning for Human Activity Recognition	May 31, 2021	Activity PredictionActivity Recognition	CodeCode Available	1
Quantization and Deployment of Deep Neural Networks on Microcontrollers	May 27, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	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