SOTAVerified

Real-Time Object Detection

Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.

This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.

( Image credit: CenterNet )

Papers

Showing 231240 of 259 papers

TitleStatusHype
Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic SimulationCode0
R-FCN: Object Detection via Region-based Fully Convolutional NetworksCode0
Real Time Object Detection System with YOLO and CNN Models: A ReviewCode0
DeNet: Scalable Real-time Object Detection with Directed Sparse SamplingCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
Real-time object detection method based on improved YOLOv4-tinyCode0
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous DrivingCode0
DaDe: Delay-adaptive Detector for Streaming PerceptionCode0
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an exampleCode0
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute SystemsCode0
Show:102550
← PrevPage 24 of 26Next →

No leaderboard results yet.