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 2130 of 259 papers

TitleStatusHype
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-IDCode2
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection0
Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)0
DEIM: DETR with Improved Matching for Fast ConvergenceCode5
Optimizing Edge Offloading Decisions for Object DetectionCode1
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving0
D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution RefinementCode7
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava0
Show:102550
← PrevPage 3 of 26Next →

No leaderboard results yet.