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).

ased on a cascade training with just 20 negatives and positives, performance of some merit can be reached (yes, even with a small number of images). Here are three videos that show it in action, where the goal is to track the car (for navigation purposes as will be shown another day).

Local Binary Patterns With Red Car

Local Binary Patterns With White Car

Local Binary Patterns With Nearly Black Car

Note: My program’s demo should be treated as just the testing of a concept at this stage. I will later on proceed to proper training and thus good performance.

n 2004 I got rid of my television. That was 8 years ago. The reasons for this are numerous and the following new image which goes around the Internet explains the resultant situation extremely well.

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.

sing androidscreencast is simple. As their site puts it, the steps to follow are as follows:

- Install the android sdk
- Connect your device through USB cable and ensure it’s detected with “adb devices”
- Make sure you have Java Runtime Environment 5 or later installed
- Click HERE. You can launch it by typing “javaws [jnlp file]” from a command line.

Here is an example video I took on a tablet:

Keywords: androidscreencast android linux tablet

he videos show a first attempt to demonstrate the application. It is difficult to demonstrate without dumping a stream of frames directly from the tablet, thus obtaining a proper screencast. There are 5 parts, with splitting done due to a technical issue with the microphone [1, 2, 3, 4, 5] (10 minutes or so in total).

I’ll look for a better way to demonstrate it. Someone told me there is a screencasts app for Android.

Keywords: android opencv ics archos linux Mobile Phone Wireless electronics Cell mobiledevice haar classification tracking machinelearning eclipse computervision research dalvik java Robot