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 3140 of 259 papers

TitleStatusHype
Focal Loss for Dense Object DetectionCode2
Real-time Object Detection for Streaming PerceptionCode2
Slim-neck by GSConv: A lightweight-design for real-time detector architecturesCode2
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Non-deep NetworksCode1
End-to-End Object Detection with TransformersCode1
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight AttentionCode1
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Show:102550
← PrevPage 4 of 26Next →

No leaderboard results yet.