Playing around with the parameters of HoughLinesP function will help you to improve the performance a bit in the cost of precision. Performance will drastically reduce for this function when the image size increases.
If possible, use HoughLines instead of probabilistic approach as it is faster.
Downscaling the image using bilinear interpolation will not effect the quality of the output as hough transformation is carried out on canny edge detector output.
The steps that I would follow will be:
- Read a frame.
- Convert to grayScale.
- Downscale the gray image.
- If possible, select the ROI on the gray image on which lane is to be detected.
- Do canny on the ROI image.
- Do hough transformation.
As you are doing lane detection algorithm I shall put my two cents in. Canny detection alone will not be of much help on road which contains shadows of trees etc as there will be edges detected around it. Though Probabilisitic Hough approach reduces the error in the above circumstances, (a) Limiting the theta value, (b) using sobel edge detection in which dx is given more priority than dy are some experiments worth trying.