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 5160 of 259 papers

TitleStatusHype
Non-deep NetworksCode1
FOVEA: Foveated Image Magnification for Autonomous NavigationCode1
YOLOX: Exceeding YOLO Series in 2021Code1
CBNet: A Composite Backbone Network Architecture for Object DetectionCode1
Towards Light-weight and Real-time Line Segment DetectionCode1
You Only Learn One Representation: Unified Network for Multiple TasksCode1
Contour Proposal Networks for Biomedical Instance SegmentationCode1
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing ImagesCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
Show:102550
← PrevPage 6 of 26Next →

No leaderboard results yet.