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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

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Showing 171–180 of 259 papers

TitleStatusHype
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement—0
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster—0
Factorial Convolution Neural Networks—0
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation—0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
Parallel Detection for Efficient Video Analytics at the EdgeCode0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets—0
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL—0
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search—0
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve DetectionCode0
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