Activity Recognition
254 papers with code • 4 benchmarks • 29 datasets
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
Benchmarks
These leaderboards are used to track progress in Activity Recognition
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Libraries
Use these libraries to find Activity Recognition models and implementationsDatasets
GazeFollow
MotionSense
Drive&Act
RWF-2000
First-Person Hand Action Benchmark
MoVi
MSR ActionPairs
Home Action Genome
Subtasks
Latest papers
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
john828/mifi
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Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems.
WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
huangshk/wimans
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WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare.
A Review of Deep Learning Methods for Photoplethysmography Data
In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions.
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
event-ahu/uncertainty_aware_mobileformer
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The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e. g., low illumination, motion blur).
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
camma-public/multibypass140
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The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)).
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
ericssonresearch/uda-4-tsc
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Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data.
Multi-stage Learning for Radar Pulse Activity Segmentation
Radio signal recognition is a crucial function in electronic warfare.
Enhanced Spatio- Temporal Image Encoding for Online Human Activity Recognition
It can be done using 3D skeleton data extracted from a RGB+D camera.
Towards a geometric understanding of Spatio Temporal Graph Convolution Networks
In this paper, we first propose to use a local Dataset Graph (DS-Graph) obtained from the feature representation of input data at each layer to develop an understanding of the layer-wise embedding geometry of the STGCN.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
kpeng9510/os-sar
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In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
