SOTAVerified

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 221230 of 259 papers

TitleStatusHype
Looking Fast and Slow: Memory-Guided Mobile Video Object DetectionCode0
Visual Mesh: Real-time Object Detection Using Constant Sample DensityCode0
CornerNet-Lite: Efficient Keypoint Based Object DetectionCode0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
Role of Spatial Context in Adversarial Robustness for Object DetectionCode0
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve DetectionCode0
DEYOv3: DETR with YOLO for Real-time Object DetectionCode0
Receptive Field Block Net for Accurate and Fast Object DetectionCode0
Relief R-CNN : Utilizing Convolutional Features for Fast Object DetectionCode0
Replication Study and Benchmarking of Real-Time Object Detection ModelsCode0
Show:102550
← PrevPage 23 of 26Next →

No leaderboard results yet.