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

Showing 251259 of 259 papers

TitleStatusHype
Relief R-CNN : Utilizing Convolutional Features for Fast Object DetectionCode0
Voting for Voting in Online Point Cloud Object Detection0
You Only Look Once: Unified, Real-Time Object DetectionCode1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors0
ARTOS -- Adaptive Real-Time Object Detection System0
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines0
Fast Object Detection with Entropy-Driven Evaluation0
Asymmetric Pruning for Learning Cascade Detectors0
Show:102550
← PrevPage 6 of 6Next →

No leaderboard results yet.