Night and Day

I have been gathering more training data and the increase from 650 total images to 740 has made a visible difference in the detection rates for the KNN classifier.

I have been experimenting with the idea of splitting the image set into smaller sets related to the time of day and general lighting level. Other than the benefits of reducing the KNN classification time, these time-specific images sets noticeably increase the detection rates. The following is results of my tests so far:

KNN (K=3)

All Images:

1. 79% accuracy, # test images = 148
2. 80% accuracy, # test images = 147
3. 84% accuracy, # test images = 148
4. 78% accuracy, # test images = 148
5. 78% accuracy, # test images = 149

Day Images:

1. 80% accuracy, # test images = 107
2. 83% accuracy, # test images = 107
3. 66% accuracy, # test images = 106
4. 82% accuracy, # test images = 107
5. 78% accuracy, # test images = 109

Night Images:

1. 90% accuracy, # test images = 41
2. 90% accuracy, # test images = 41
3. 90% accuracy, # test images = 41
4. 82% accuracy, # test images = 41
5. 80% accuracy, # test images = 41

SVM

All Images:

1. 78% accuracy, # test images = 148
2. 76% accuracy, # test images = 147
3. 78% accuracy, # test images = 148
4. 68% accuracy, # test images = 148
5. 76% accuracy, # test images = 149

Day Images:

1. 75% accuracy, # test images = 107
2. 76% accuracy, # test images = 107
3. 59% accuracy, # test images = 106
4. 75% accuracy, # test images = 107
5. 66% accuracy, # test images = 109

Night Images:

1. 70% accuracy, # test images = 41
2. 85% accuracy, # test images = 41
3. 68% accuracy, # test images = 41
4. 75% accuracy, # test images = 41
5. 63% accuracy, # test images = 41