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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

Showing 171180 of 259 papers

TitleStatusHype
Object detection and tracking benchmark in industry based on improved correlation filter0
Object Detection in Specific Traffic Scenes using YOLOv20
Object Detection in the Context of Mobile Augmented Reality0
Octave-YOLO: Cross frequency detection network with octave convolution0
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing0
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems0
PP-YOLOv2: A Practical Object Detector0
Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection0
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy0
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