SOTAVerified

Motion Detection

Motion Detection is a process to detect the presence of any moving entity in an area of interest. Motion Detection is of great importance due to its application in various areas such as surveillance and security, smart homes, and health monitoring.

Source: Different Approaches for Human Activity Recognition– A Survey

Papers

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Showing 76–100 of 101 papers

TitleStatusHype
Unsupervised Online Video Object Segmentation with Motion Property UnderstandingCode0
WisenetMD: Motion Detection Using Dynamic Background Region Analysis—0
A Robust Background Initialization Algorithm with Superpixel Motion Detection—0
A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds—0
Comparative study of motion detection methods for video surveillance systemsCode0
Evolution leads to a diversity of motion-detection neuronal circuits—0
Learning-Based Quality Control for Cardiac MR Images—0
Event-based Moving Object Detection and Tracking—0
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep LearningCode0
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving—0
Independent Motion Detection with Event-driven Cameras—0
Multi-Class Model Fitting by Energy Minimization and Mode-SeekingCode0
Change Detection under Global Viewpoint Uncertainty—0
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian ModelsCode0
A Novel Motion Detection Method Resistant to Severe Illumination Changes—0
Multi-Scale Saliency Detection using Dictionary Learning—0
Automatic detection of moving objects in video surveillance—0
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos—0
“Eyes Open – Eyes Closed” EEG/fMRI data set including dedicated “Carbon Wire Loop” motion detection channels—0
Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection—0
Tracking Motion and Proxemics using Thermal-sensor Array—0
On a spatial-temporal decomposition of the optical flow—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling—0
Multiple-object tracking in cluttered and crowded public spaces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FastFlowNet (Kitti)F1 (%)92.9—Unverified
2Raft (Kitti)F1 (%)89.5—Unverified