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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 131140 of 259 papers

TitleStatusHype
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster0
RegionCLIP: Region-based Language-Image PretrainingCode1
Factorial Convolution Neural Networks0
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUsCode1
Non-deep NetworksCode1
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain0
FOVEA: Foveated Image Magnification for Autonomous NavigationCode1
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception ChallengeCode3
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