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 191200 of 259 papers

TitleStatusHype
StreamYOLO: Real-time Object Detection for Streaming Perception0
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection0
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers0
Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception0
USB: Universal-Scale Object Detection Benchmark0
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks0
Voting for Voting in Online Point Cloud Object Detection0
WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras0
What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector0
0/1 Deep Neural Networks via Block Coordinate Descent0
Show:102550
← PrevPage 20 of 26Next →

No leaderboard results yet.