Computer Vision III: Detection, Tracking, Segmentation at TU München

Flashcards and summaries for Computer Vision III: Detection, Tracking, Segmentation at the TU München

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Study with flashcards and summaries for the course Computer Vision III: Detection, Tracking, Segmentation at the TU München

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Briefly describe Viola-Jones Detector

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Briefly describe procedure of Histogram of Oriented Gradients

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Name Three Classical Object Detection Methods

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Briefly describe the algorithm of Non-Max Suppression 

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Name several one-stage detectors

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Name several two-stage detectors

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Which datasets are used to evaluate object detection methods?

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Describe the training scheme for R-CNN

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

What is the main contribution of the SPP-Net?

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

How does the Fast R-CNN work?

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

How does a Region Proposal Network work?

Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

What is YOLO and how does it work?

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Exemplary flashcards for Computer Vision III: Detection, Tracking, Segmentation at the TU München on StudySmarter:

Computer Vision III: Detection, Tracking, Segmentation

Briefly describe Viola-Jones Detector

- learn multiple weak learners on Haar-like features

- combine result of weak learners to form strong final decision

- use AdaBoost to find best combination of weak learners

Computer Vision III: Detection, Tracking, Segmentation

Briefly describe procedure of Histogram of Oriented Gradients

- choose images that contain object you want to detect

- choose images that do NOT contain said object

- compute HOG for each image

- train SVM on HOG to separate images with object and images without object

Computer Vision III: Detection, Tracking, Segmentation

Name Three Classical Object Detection Methods

- Template matching + sliding window

- Viola-Jones Detector

- Histogram of Oriented Gradients (HOG)

Computer Vision III: Detection, Tracking, Segmentation

Briefly describe the algorithm of Non-Max Suppression 

- Algo 

for box in AllBoxes:

  for otherBox in AllBoxes:

   if overlap(box, otherBox):

    keepBetterBox(score(box), score(otherBox))


- overlap is IoU

- score depends on task

- NMS used in many tasks


Computer Vision III: Detection, Tracking, Segmentation

Name several one-stage detectors

- YOLO

- Single-Shot Multiscale Detector (SSD)

- RetinaNet

- CornerNet

- CenterNet

- ExtremeNet

Computer Vision III: Detection, Tracking, Segmentation

Name several two-stage detectors

- R(egion)-CNN

- Fast R-CNN

- Faster R-CNN

- SPP-Net

- R-FCN

- FPN

Computer Vision III: Detection, Tracking, Segmentation

Which datasets are used to evaluate object detection methods?

- PASCAL VOC 2007-12

- ImageNet 2010-17

- COCO 2015-

- OpenImages 2018-

Computer Vision III: Detection, Tracking, Segmentation

Describe the training scheme for R-CNN

1. Pretrain CNN on ImageNet

2. Fine-tune CNN on classes that you are actually trying to predict

3. Train one SVM per class on image regions.

4. Train bounding box regressor (L2 loss)

Computer Vision III: Detection, Tracking, Segmentation

What is the main contribution of the SPP-Net?

Instead of passing every proposal region through a separate CNN, the entire image is passed through a single CNN

Computer Vision III: Detection, Tracking, Segmentation

How does the Fast R-CNN work?

- like SPP-Net it applies a CNN on entire image

- the proposals are then extracted from the feature map and warped using ROI pooling

- to warp the features, one applies a grid of fixed size on each proposal (backprop like max-pooling)

Computer Vision III: Detection, Tracking, Segmentation

How does a Region Proposal Network work?

- Per pixel in the feature map, you have 9 anchors (3 differently sized boxes at 3 different ratios)

- the proposals get refined via regression (L1 loss) and classified as object/non-object

Computer Vision III: Detection, Tracking, Segmentation

What is YOLO and how does it work?

- You Only Look Once is a one-stage detector

- there are no object proposals but detection and box regression is done directly from feature map

- for this, the image is divided into grid and multiple boxes are set in the grid (~100k proposals)

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