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.

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.

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

ascade 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

n 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:

ar navigation using computer vision algorithms/programs (as opposed to GPS/maps) is scarcely explored in the form of mobile applications. With many built-in cameras and increasing processing power/RAM it would be desirable to exploit — to the extent possible — whatever general-purpose devices have to offer while idle; single-purpose appliances like TomTom make less business sense nowadays and development frameworks for mobile platforms have become versatile enough to empower third-party developers. Based on conversations with colleagues, OpenCV and its many plugins should be somehow available for Android as well, albeit it may require some hacking and adaptation to the hardware at hand (high-end ARM for the most part).

If the goal is to make vehicles with cameras mounted onto them interpret a scene like humans do, then analysis of video sequences on mobile hardware (efficient applications) ought to be explored, with special emphasis on performance. C++ has little memory footprint and high efficiency. Contemporarily, resolution at a high capture rate is satisfactory enough for the task, but it is unclear whether a good algorithm that segments and tracks a scenes can keep up. A GPU-like processing power is available on some phones, but not all (drivers for non-x86 architectures are poor or scarce, too). MobileEye offers peripheral and assistive hardware for this reason, recognising the known caveats.Vuforia does augmented reality for mobile platforms and a company called ThirdSight also makes mobile applications with computer vision methodologies. Not so long ago (April 2010) it was reported that “development of new automobile safety features and military applications [...] could save lives.” The hardware is not specified in the report. To quote, “Snyder and his co-authors have written a program that uses algorithms to sort visual data and make decisions related to finding the lanes of a road, detecting how those lanes change as a car is moving, and controlling the car to stay in the correct lane.”

While purely automatic driving is currently verboten, computer-aided driving is legal and forms a growing trend. It need not involve any mechanics either, as it’s most about message-passing to a human (HCI).

omputer Vision is definitely made possible on Android using OpenCV. Here is an android-opencv demo app [via] which may come handy for programming in C/C++. This further and latest exploration complements the earlier post as car navigation-targeted open source code is absent; what we currently have out there mostly uses maps, not image/video, so there is a gap that would augment an open source car, e.g. with open source navigation that incorporates widely-researched methods. Dashboard Cam, an Android application which is demostrated here, uses GPS and also uses photo overlays, but there is no computer vision/pattern recognition work being done.