Vision Based Traffic Light Triggering for Motorbikes Report
The goal of this project was to create a computer vision algorithm to detect incoming motorbike traffic for traffic light triggering.
I intended to partially track the motorbike enough for it to distinguish it from cross traffic and noise.
Problems I ran into and steps I took to correct them:
1. I choose to use video processing rather than single image processing. Video footage allows motion segmentation by background subtraction.
2. Defining the background image for subtraction was also a problem. A set image is not very robust. a slight movement to the camera will produce noise. Just using the previous frame will not give enough difference. Elected to use a sliding average to determine the background image.
2. Labeling the foreground blobs for tracking. Accomplished by computing the distance from previous and attaching it to nearest one within a threshold. If not, create a new label.
3. Lighting conditions does adversely affect motion segmentation. Solved by using different colorspaces to get reduce of the influence of lighting.
RGB
B/Y Opponent
4. Simple Tracking methods are easily affected by various factors. Cross traffic can easily steal the intended tracking(or vice versa). I determined that even if the tracking does not entirely work, sometimes the partial tracking maybe enough to do the job. In my case, I was able to use RANSAC to determine the partial trajectories.
Steps I did not have time for:
- Actual classification system. Now the traffic detection problem is reduced to a line fitting/classification problem. Ideas include feeding in annotated correct trajectories and testing by a difference on the test data.
- Extensive training/testing set.
- Should have researched more on other methods at the various steps. Instead of tracking by labeling by area, perhaps should have tried interest point detection(as used by the other groups) and following a region of interest points moving in the same direction.
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