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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 111120 of 259 papers

TitleStatusHype
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionCode0
Fast object detection in compressed JPEG ImagesCode0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
PVANet: Lightweight Deep Neural Networks for Real-time Object DetectionCode0
Fast and accurate object detection in high resolution 4K and 8K video using GPUsCode0
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve DetectionCode0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
Receptive Field Block Net for Accurate and Fast Object DetectionCode0
BlitzNet: A Real-Time Deep Network for Scene UnderstandingCode0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
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