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:
- 79% accuracy, # test images = 148
- 80% accuracy, # test images = 147
- 84% accuracy, # test images = 148
- 78% accuracy, # test images = 148
- 78% accuracy, # test images = 149
- 80% accuracy, # test images = 107
- 83% accuracy, # test images = 107
- 66% accuracy, # test images = 106
- 82% accuracy, # test images = 107
- 78% accuracy, # test images = 109
- 90% accuracy, # test images = 41
- 90% accuracy, # test images = 41
- 90% accuracy, # test images = 41
- 82% accuracy, # test images = 41
- 80% accuracy, # test images = 41
SVM
All Images:
- 78% accuracy, # test images = 148
- 76% accuracy, # test images = 147
- 78% accuracy, # test images = 148
- 68% accuracy, # test images = 148
- 76% accuracy, # test images = 149
- 75% accuracy, # test images = 107
- 76% accuracy, # test images = 107
- 59% accuracy, # test images = 106
- 75% accuracy, # test images = 107
- 66% accuracy, # test images = 109
- 70% accuracy, # test images = 41
- 85% accuracy, # test images = 41
- 68% accuracy, # test images = 41
- 75% accuracy, # test images = 41
- 63% accuracy, # test images = 41
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