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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 101 papers

TitleStatusHype
WiFi Motion Detection: A Study into Efficacy and Classification—0
WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS—0
Wireless LAN sensing with smart antennas—0
2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses—0
A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments—0
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds—0
Enabling energy efficient machine learning on a Ultra-Low-Power vision sensor for IoT—0
Event-based Moving Object Detection and Tracking—0
Evolution leads to a diversity of motion-detection neuronal circuits—0
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study—0
“Eyes Open – Eyes Closed” EEG/fMRI data set including dedicated “Carbon Wire Loop” motion detection channels—0
Geometric Change Detection in Digital Twins using 3D Machine Learning—0
HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture—0
Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm Regularizations—0
Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering—0
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning—0
Independent Motion Detection with Event-driven Cameras—0
Intelligent Video Recording Optimization using Activity Detection for Surveillance Systems—0
Learning-Based Quality Control for Cardiac MR Images—0
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones—0
Model-free Tracking with Deep Appearance and Motion Features Integration—0
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving—0
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning—0
Deep Learning for Inertial Sensor Alignment—0
Multi-Beam Integrated Sensing and Communication: State-of-the-Art, Challenges and Opportunities—0
Show:102550
← PrevPage 3 of 5Next →

Benchmark Results

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