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Archive for the ‘Research’ Category

Extensive Static Test of Car Tracker (for Navigation/Collision) and Tracking With Increased Sample Size

BASED 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

Tiny Set for Training of a Local Binary Patterns (LBP) Classifier

Based 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).

Local Binary Patterns in Action

Based 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.

Car Navigation Single Car Classifier

With 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.

Classification on Android Tablet – Parts 1-5

The 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

Cascade Classification in OpenCV – Parts 1-9

Cascade classification is easy to work with in OpenCV, but it is not so well documented. This series of short videos explains how this is done on GNU/Linux-based systems (although it may be useful and applicable to other platforms too). The videos were not scripted or planned, so please excuse the occasional stuttering and mistakes.

Keywords: OpenCV android linux gnu ubuntu cmake eclipse computervision research haar tracking machinelearning

OpenCV and Car Identification

In order for identification of vehicles to work (for navigation, not Big Brother application) I am preparing a paper on some existing methods. Along the way I found some interesting videos. The first one shows OpenCV on Android phones:

OpenCV on desktop hardware can achieve more, with high-resolution images as well:

Vehicle classification (not OpenCV):

For collision prevention it helps to estimate speeds of relative speed of nearby vehicles. Here is an application which is measuring vehicle speed on the fly:

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Original styles created by Ian Main (all acknowledgements) • PHP scripts and styles later modified by Roy Schestowitz • Help yourself to a GPL'd copy
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