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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 251–259 of 259 papers

TitleStatusHype
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
R-FCN: Object Detection via Region-based Fully Convolutional NetworksCode0
Relief R-CNN : Utilizing Convolutional Features for Fast Object DetectionCode0
Voting for Voting in Online Point Cloud Object Detection—0
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors—0
ARTOS -- Adaptive Real-Time Object Detection System—0
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines—0
Fast Object Detection with Entropy-Driven Evaluation—0
Asymmetric Pruning for Learning Cascade Detectors—0
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