1. Week 6
Cecilia La Place

2. Progress so far... Created 5 different train-val-test splits
Trained on split 1 and 2
Tested split 1
Training on split 3
Testing split 2

3. Papers Read Papers read
Sky Region Detection in a Single Image for Autonomous Ground Robot Navigation - Shen et al
Horizon Detection Using Machine Learning Techniques - Fefiltyev et al
Convolution LSTM Network: A Machine Learning Approach for Precipitation Nowcasting - Shi et al

4. Shen et al Discusses different sky segmentation methods
Need faster image processing for computers not as powerful such as small robots
Highlights problems in sky segmentation
Proposes method for faster image processing
Preliminary information from a gradient
Preliminary sky segmentation
Combines the two to achieve a better sky segmentation

5. Fefiltyev et al Sky segmentation using machine learning classifiers
SVM, J48, Naive Bayes
Detects horizon lines
Small dataset (20 images)
Goal is to classify sky and ground pixels presuming common knowledge of where the sky is

6. Shi et al
Nowcasting is the precise and timely prediction of weather and a problem in weather forecasting
Approaches the problem from a machine learning perspective
Applies fully connected LSTM which has temporal but not spatial information
Proposes new model the convolution LSTM to account for the spatial information as well

7. Data 28 cameras for training 4 cameras for validation
13 cameras for testing
On average based on mIOU split 1 had an accuracy of 43.3%
The model is recognizing and trying to segment on nighttime and low light photos (better than Cityscapes!)
Outperforms some cameras evaluated by Cityscapes

8. Split 1 - IOU and more Camera Cityscapes Refinenet 3395 49.25% 59.96%
8733
54.01%
58.80%
17218
31.70%
49.42%
1093
62.56%
39.81%
4679
58.75%
28.80%

9. Split 1 - Visual

10. Split 1 - Visual cont.

11. To do Gather more resources for literature review
Start laying out poster for the poster presentations
Train splits 4 and 5
Retrain split 2 due to overfitting
Test split 2-5