Explore object detection techniques in this comprehensive computer vision lecture. Delve into the comparison of detection algorithms, examining R-CNN training methods including fine-tuning, feature extraction, and classifier training. Investigate Fast R-CNN's improvements, focusing on Region of Interest Pooling. Analyze the limitations of Fast R-CNN and discover the advancements made in Faster R-CNN, including the introduction of Region Proposal Networks (RPN) and anchor boxes. Gain valuable insights into the evolution of object detection methodologies within the field of computer vision.
Overview
Syllabus
Intro
Which algorithm is better?
Detection as Classification
R-CNN Training (Fine-tuning)
R-CNN Training (feature extraction)
R-CNN Training (train classifier)
UCF R-CNN Training (bounding box regression/prediction)
Issue #1 with R-CNN
Fast R-CNN: Another view
Fast R-CNN: Region of Interest Pooling
ROI-Pooling
Problem of Fast R-CNN?
R-CNN Summary
Region proposal network (RPN)
Anchor boxes
Faster R-CNN
Taught by
UCF CRCV
Tags
Related Courses
-
Computer Vision: YOLO Custom Object Detection with Colab GPU
-
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!
-
Action Recognition, Temporal Localization and Detection in Videos
-
Object and Bounding Box Detection with Vision Models Using Florence 2
-
Computer Vision
-
Convolutional Neural Networks
