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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 141150 of 259 papers

TitleStatusHype
Parallel Detection for Efficient Video Analytics at the EdgeCode0
YOLOX: Exceeding YOLO Series in 2021Code1
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets0
CBNet: A Composite Backbone Network Architecture for Object DetectionCode1
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search0
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL0
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve DetectionCode0
Towards Light-weight and Real-time Line Segment DetectionCode1
You Only Learn One Representation: Unified Network for Multiple TasksCode1
PP-YOLOv2: A Practical Object Detector0
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