Abstract:
The presence of raindrop induced distortion can have a significant negative impact
on computer vision applications. Here we address the problem of visual raindrop
distortion in standard colour video imagery for use in non-static, automotive
computer vision applications where the scene can be observed to be changing over
subsequent consecutive frames. We utilise current state of the art research
conducted into the investigation of salience mapping as means of initial detection
of potential raindrop candidates. We further expand on this prior state of the art
work to construct a combined feature rich descriptor of shape information (Hu
moments), isolation of raindrops pixel information from context, and texture
(saliency derived) within an improved visual bag of words verification framework.
Support Vector Machine and Random Forest classification were utilised for
verification of potential candidates, and the effects of increasing discrete cluster
centre counts on detection rates were studied.
This novel approach of utilising extended shape information, isolation of context,
and texture, along with increasing cluster counts, achieves a notable 13% increase
in precision (92%) and 10% increase in recall (86%) against prior state of the art.
False positive rates were also observed to decrease with a minimal false positive
rate of 14% observed.
