Object Detection Part 1: R-CNN, Sliding Window and Selective Search

This is the first video in the object detection series and in it we are exploring the definition of object detection in computer vision, how we can approach this task using the sliding window algorithm and how the Region-based Convolutional Neural Network (R-CNN) model improves this approach by employing the selective search region proposal algorithm.

*References*
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"Rich feature hierarchies for accurate object detection and semantic segmentation" paper: https://arxiv.org/abs/1311.2524
"Selective Search for Object Recognition" paper: http://www.huppelen.nl/publications/selectiveSearchDraft.pdf
Selective search algorithm (more details): https://learnopencv.com/selective-search-for-object-detection-cpp-python/

*Related Videos*
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Object Detection Part 2: Fast R-CNN, Region Projection and Region of Interest (RoI) Pooling Layer: https://youtu.be/mOi5VDRLVH0
Object Detection Part 3: Faster R-CNN, Region Proposal Network and Intersection over Union: https://youtu.be/68VE1gebfVc
Why Neural Networks Can Learn Any Function: https://youtu.be/O45AaRPQhuI
Why Deep Neural Networks (DNNs) Underperform Tree-Based Models on Tabular Data: https://youtu.be/e62CBva4TYc
Why Residual Connections (ResNet) Work: https://youtu.be/Gey9CG6R6w8

*Contents*
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00:00 - Intro
00:14 - Object Detection Definition
00:59 - Sliding Window
02:00 - R-CNN Model
02:32 - Selective Search Algorithm
04:27 - Cons of Using R-CNN
05:20 - Outro

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Видео Object Detection Part 1: R-CNN, Sliding Window and Selective Search канала DataMListic