In this tutorial we will look at vehicle tracking using haar features. We have a haar cascade file trained on cars.
The program will detect regions of interest, classify them as cars and show rectangles around them.
Lets start with the basic cascade detection program:
This will detect cars in the screen but also noise and the screen will be jittering sometimes. To avoid all of these, we have to improve our car tracking algorithm. We decided to come up with a simple solution.
Car tracking algorithm
For every frame:
- Detect potential regions of interest
- Filter detected regions based on vertical,horizontal similarity
- If its a new region, add to the collection
- Clear collection every 30 frames
Removing false positives
The mean square error function is used to remove false positives. We compare vertical and horizontal sides of the images. If the difference is to large or to small it cannot be a car.
A car may not be detected in every frame. If a new car is detected, its added to the collection.
We keep this collection for 30 frames, then clear it.
The cascades are not rotation invariant, scale and translation invariant. In addition, Detecting vehicles with haar cascades may work reasonably well, but there is gain with other algorithms (salient points).
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