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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

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Showing 101–110 of 259 papers

TitleStatusHype
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection—0
Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)—0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving—0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava—0
Fast Object Detection with a Machine Learning Edge Device—0
What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector—0
Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception—0
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an exampleCode0
Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approach—0
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