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 871–880 of 1322 papers

Title	Date	Tasks	Status
Spectrum-Guided Adversarial Disparity Learning	Jul 14, 2020	Activity RecognitionDenoising	CodeCode Available
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos	Jul 13, 2020	Activity RecognitionGroup Activity Recognition	—Unverified
Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing	Jul 13, 2020	Activity RecognitionEvent Detection	—Unverified
Transfer Learning for Activity Recognition in Mobile Health	Jul 12, 2020	Activity RecognitionTransfer Learning	CodeCode Available
An Efficient Data Imputation Technique for Human Activity Recognition	Jul 8, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
ARC-Net: Activity Recognition Through Capsules	Jul 6, 2020	Activity RecognitionARC	—Unverified
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization	Jul 6, 2020	Activity RecognitionContinual Learning	—Unverified
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos	Jul 6, 2020	Activity RecognitionGroup Activity Recognition	—Unverified
Handling Variable-Dimensional Time Series with Graph Neural Networks	Jul 1, 2020	Activity RecognitionGraph Neural Network	—Unverified
Human Activity Recognition based on Dynamic Spatio-Temporal Relations	Jun 29, 2020	Activity RecognitionHuman Activity Recognition	—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