Update: Today is the last day of the year and here’s what the data (latest) looks like today:

Some “surge”, eh?

Update #2 (January 7th): 12 days have passed. Let’s revisit the data from the official source.

Is this a surge?

ITTLE, CONCISE, but completely new presentation about work that I did in 2010-2011. Cardiac Stress Analysis in 4-D is the title (PDF, ODF).

T IS the end of an era as another project comes to an important milestone. I was preparing a lot of code for upload last week. I wanted to wait a while, at the very least until I had also uploaded the accompanying papers. A 2011-2012 Technical Report [HTML, PDF] about Identity Verification and “Car Navigation Through Computer Vision Methods With Rudimentary Implementation Under Android” [HTML, PDF] about Car Navigation have been uploaded. Due to some server error I am still trying to gather all the code for the former in order to upload it. But that too will come soon.

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.

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.

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.

Initial Test

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).