This week I worked on further editing the precision of vehicle detection. Our tracking system works pretty well but precision of vehicle detection is still a bit of an issue.
Here are some of the edge cases I worked on:
- Stacked Vehicles
The issue associated with this case is that stacked vehicles prevent the vehicle in the rear from getting picked up until way further when the cars separate.
This is the post back ground subtraction mask. Clearly our system cannot discern that these are two separate vehicles as it mainly uses background subtraction to detect objects on the road.
Solutions I explored for this include utilizing the different colors of the vehicle to temporarily discern that two seperate objects exist.
2) Shadows
Harsh shadows create artificially large bounding boxes as the “background” is being covered by the shadow as seen in this image of the truck. Things become worse in videos captured during hours with “harsh” shadows.
Solutions to harsh shadows include utilizing a “darkened” background subtraction on top of the current background subtraction. This is still a work in progress but essentially we can darken the background and further mask shadows out using the darkened background. Additionally, in this image it is shown that the shadow can be seen “outside of the image of the road.” We are currently working on being able to “mask out” non road pixels when determining contours.