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 841–850 of 1322 papers

Title	Date	Tasks	Status	Hype
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network	Oct 7, 2020	Activity Recognitionfeature selection	—Unverified	0
Deep learning for time series classification	Oct 1, 2020	Activity RecognitionClassification	CodeCode Available	2
Attention-Driven Body Pose Encoding for Human Activity Recognition	Sep 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Semi-supervised sequence classification through change point detection	Sep 24, 2020	Activity RecognitionChange Point Detection	—Unverified	0
Stacked Generalization for Human Activity Recognition	Sep 22, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model	Sep 20, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations	Sep 16, 2020	Activity RecognitionVideo Classification	—Unverified	0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone	Sep 8, 2020	Activity RecognitionReinforcement Learning (RL)	—Unverified	0
Personalization in Human Activity Recognition	Sep 1, 2020	Activity RecognitionDiversity	—Unverified	0
A benchmark of data stream classification for human activity recognition on connected objects	Aug 27, 2020	Activity RecognitionClassification	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