ed and green hues represent the matching from dual-scale classifiers of the rear of cars. Some more bugs were removed in this latest iteration of the implementation.
Archive for the ‘Android’ Category
he circle at the left shows whether the car is getting closer (white) or going away (red). The size of the circle is indicative of length.
esting shown on various cars excluded from the training set. I will soon release a PDF document to explain what my program is achieving and how.
Extensive Static Test of Car Tracker (for Navigation/Collision) and Tracking With Increased Sample Size
ASED on further experiments, at the expense of performance in terms of framerate we can easily improve accuracy to the point of perfect tracking for particular cars. This is done by increasing the sample (window) size on which the tree is trained.
Using 20 positives and only 10 negatives for training.
20 positives and 20 negatives with a sample size of 40×40
ased on cascade training with just 10 negatives and 20 positives (various cars and distances, poses). The real FPS rate is 6+; in the demo it’s decreased by the grabbing/streaming of screenshots for video capturing.
Zoom Changes and Local Binary Patterns Classifier Applied to Red Car
Car Tracking Test (Static With Panning)
6 FPS for tracking (1-minute video)
This video shows tracking of a car based on training with just 10 negatives and 20 positives (without cars like the one in this demo). The real FPS rate is around 6; grabbing and saving a video such as this (in real time) entails a massive performance penalty, so this demo cannot show just how smooth the tracking really is. For a classifier trained on more examples the performance will be comparable. If some code cruft is removed and the rendering gets optimised, 8 FPS seems reachable (this device generally captures raw video at about 10 FPS).
ith a training set of just a dozen positives from a single car I have let the experiment run. The purpose of this experiment is to test the alarm (collision) mechanism for short-range D alone.
Dashboard and tracking enriched somewhat, footage on Motorola Droid (captured by someone else). I’ve not gotten around to implementing better tracking yet.