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 131–140 of 259 papers

TitleStatusHype
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data—0
Deriving star cluster parameters with convolutional neural networks. II. Extinction and cluster/background classification—0
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting—0
Detection of Rail Line Track and Human Beings Near the Track to Avoid Accidents—0
DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios—0
Dynamic Zoom-in Network for Fast Object Detection in Large Images—0
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving—0
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems—0
Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)—0
Factorial Convolution Neural Networks—0
Show:102550
← PrevPage 14 of 26Next →

No leaderboard results yet.