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    <title>RoboRealm Forum</title>
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    <lastBuildDate>Tue, 30 Nov 1999 00:11:00 EST</lastBuildDate>
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    <item>
        <title>No Title</title>
        <description>
        Sam,&lt;br&gt;&lt;br&gt;Certainly the easiest way is to detect motion from a static camera (using the movement module) and use that to isolate areas of change in the image. Counting those areas gives you a rough idea on how many cars are going by. Based on the relative size of those movement areas you can guess at car/bike/lorry.&lt;br&gt;&lt;br&gt;Using shape or object recognition is harder for that task since there are many models of cars/bikes/etc. that you would have to train on ... which is possible but takes a lot more work.&lt;br&gt;&lt;br&gt;STeven.        </description>
        <link>http://www.roborealm.com/forum/index.php?thread_id=4563</link>
        <guid>http://www.roborealm.com/forum/index.php?thread_id=4563</guid>
        <pubDate>Tue, 30 Nov 1999 00:11:00 EST</pubDate>
    </item>
    <item>
        <title>Detection of Cars and Motobikes</title>
        <description>
        Hi everyone,&lt;br&gt;&lt;br&gt;I&apos;m just posting up ask you lot with the experiences what would be the best way of detecting cars and motorbikes on a live camera?&lt;br&gt;&lt;br&gt;I&apos;ve bought the software of which can i say is FANTASTIC. Dead easy to learn on etc Had a play with it last night and have try blob and canny etc and it&apos;s real good :D&lt;br&gt;&lt;br&gt;All I want to do atm is be able to detect cars and a motorbikes on say a road. Later on going to expand it to find the distance of the car/bike. &lt;br&gt;&lt;br&gt;So just asking what would be the best way to detect them (cars and bikes) what filter should i use, should i use shape or object recognition&amp;nbsp;&amp;nbsp;etc&lt;br&gt;&lt;br&gt;Cheers &lt;br&gt;&lt;br&gt;Sam        </description>
        <link>http://www.roborealm.com/forum/index.php?thread_id=4563</link>
        <guid>http://www.roborealm.com/forum/index.php?thread_id=4563</guid>
        <pubDate>Tue, 30 Nov 1999 00:11:00 EST</pubDate>
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