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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 121130 of 259 papers

TitleStatusHype
Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis0
A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities0
BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation0
BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge0
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain0
Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection0
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL0
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images0
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