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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 181–190 of 259 papers

TitleStatusHype
First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria—0
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds—0
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene—0
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning—0
Globally-scalable Automated Target Recognition (GATR)—0
HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection—0
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines—0
How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection—0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava—0
Image Models for large-scale Object Detection and Classification—0
Show:102550
← PrevPage 19 of 26Next →

No leaderboard results yet.