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 161–170 of 259 papers

TitleStatusHype
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving—0
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods—0
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer—0
Liquid Leak Detection Using Thermal Images—0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection—0
Modality-Buffet for Real-Time Object Detection—0
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster—0
Object-centric Cross-modal Feature Distillation for Event-based Object Detection—0
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors—0
Show:102550
← PrevPage 17 of 26Next →

No leaderboard results yet.