Human Activity Recognition
136 papers with code • 4 benchmarks • 10 datasets
Classify various human activities
Benchmarks
These leaderboards are used to track progress in Human Activity Recognition
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Libraries
Use these libraries to find Human Activity Recognition models and implementationsDatasets
HAR
PAMAP2
WEAR
MPHOI-72
FLAG3D
HASCD
MPII Human Pose Descriptions
MOSAD
MuscleMap136
Most implemented papers
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis.
A Survey of Human Activity Recognition Using WiFi CSI
ermongroup/Wifi_Activity_Recognition
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This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers.
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
fabienbaradel/glimpse_clouds
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CVPR 2018
No spatial coherence is forced on the glimpse locations, which gives the module liberty to explore different points at each frame and better optimize the process of scrutinizing visual information.
Semi-Supervised Online Structure Learning for Composite Event Recognition
Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams.
Dynamic Vision Sensors for Human Activity Recognition
We propose to use the various slices (such as $x-y$, $x-t$, and $y-t$) of the DVS video as a feature map for HAR and denote them as Motion Maps.
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art
arturjordao/WearableSensorData
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Inspired by this, we conduct an extensive set of experiments that analyze different sample generation processes and validation protocols to indicate the vulnerable points in human activity recognition based on wearable sensor data.
Object Level Visual Reasoning in Videos
fabienbaradel/object_level_visual_reasoning
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ECCV 2018
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety.
Subject Cross Validation in Human Activity Recognition
We conclude that Human Activity Recognition systems should be evaluated by Subject Cross Validation, and that overlapping windows are not worth their extra computational cost.
Multivariate Time Series Classification using Dilated Convolutional Neural Network
SonbolYb/multivariate_timeseries_dilated_conv
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Traditional approaches employ hand-crafted features for classification while convolutional neural networks (CNN) are able to extract features automatically.
