https://sourceforge.net/p/openimaj/wiki/VideoSIFT/Recent changes to VideoSIFTenFri, 20 May 2011 13:29:09 -0000https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v15 +++ v16 @@ -3,7 +3,7 @@ * The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [here](http://www.youtube.com/watch?v=Bm5qUG-06V8). * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -If you want to try these demos yourself you'll currently need to be on a Mac or Windows machine and have a webcam (supported by Quicktime on the mac or DirectShow on Windows). You'll also need a version of Java greater than 1.6. +If you want to try these demos yourself you'll currently need to be on a Mac, Windows or Linux machine and have a webcam (supported by Quicktime on the mac, DirectShow on Windows or video for linux on linux). You'll also need a version of Java greater than 1.6. An assembled JAR with all the required dependencies can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: </pre>Jonathon HareFri, 20 May 2011 13:29:09 -0000https://sourceforge.netcb3b41cc5df0e050fcfa27fbec8406c89969d1echttps://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v14 +++ v15 @@ -1,7 +1,6 @@ The OpenIMAJ difference-of-Gaussian/SIFT implementation is quite fast. To illustrate this, we have created two demos that illustrate SIFT extraction and matching (with homography fitting) in near real-time using a webcam as an input. * The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [here](http://www.youtube.com/watch?v=Bm5qUG-06V8). - * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). If you want to try these demos yourself you'll currently need to be on a Mac or Windows machine and have a webcam (supported by Quicktime on the mac or DirectShow on Windows). You'll also need a version of Java greater than 1.6. </pre>Jonathon HareWed, 18 May 2011 17:04:23 -0000https://sourceforge.net1c6f4255794cc555551bd24e708126104295589ehttps://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v13 +++ v14 @@ -4,27 +4,23 @@ * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM, and you'll probably also need to install version 101 of WinVDIG from [here](http://shiffman.net/vdig/) so that Quicktime sees your camera (in my quick testing, the latest version (105) resulted in distorted video). - -We're currently working on a proper cross-platform (Windows, Mac & Linux; 32 and 64 versions) capture system that will mean that no extra dependencies need to be installed. Currently we have no estimate as to when that might be ready however. - -An assembled JAR with all the required dependencies (except Quicktime) can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: - -~~~~~ -:::bash +If you want to try these demos yourself you'll currently need to be on a Mac or Windows machine and have a webcam (supported by Quicktime on the mac or DirectShow on Windows). You'll also need a version of Java greater than 1.6. + +An assembled JAR with all the required dependencies can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: + +~~~~~ +:::bash java -Xmx1G -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoSIFT ~~~~~ The second demo can be run with: ~~~~~ :::bash java -Xmx1G -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoKLTSIFT ~~~~~ -(Don't forget to add the -d32 option to the commands on a Mac!) - Both demos operate in the same way; once loaded they display a live video picture. Hold the object you wish to track in front of the camera and press the spacebar to pause the video. You can then click on the video window to select the outline of the object you wish to track. Once the object is outlined, press the "c" key to capture the model, and then press the spacebar to resume the video. The object should then be tracked as you move it around. -The sourcecode for the demos is available in the subversion repository in the /trunk/demos/VideoSIFT maven project. +The sourcecode for the demos is available in the subversion repository in the `/trunk/demos/VideoSIFT` maven project. </pre>Jonathon HareWed, 18 May 2011 17:03:07 -0000https://sourceforge.net38a47442a12874788c1e413331baae86c952402bhttps://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v12 +++ v13 @@ -4,7 +4,7 @@ * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM, and you'll probably also need to install version 101 of WinVDIG from [here](http://shiffman.net/vdig/) so that Quicktime sees your camera. +If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM, and you'll probably also need to install version 101 of WinVDIG from [here](http://shiffman.net/vdig/) so that Quicktime sees your camera (in my quick testing, the latest version (105) resulted in distorted video). We're currently working on a proper cross-platform (Windows, Mac & Linux; 32 and 64 versions) capture system that will mean that no extra dependencies need to be installed. Currently we have no estimate as to when that might be ready however. </pre>Jonathon HareMon, 16 May 2011 09:23:23 -0000https://sourceforge.nete89aa835cb591379a9465373554650a4018aa3a0https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v11 +++ v12 @@ -4,7 +4,9 @@ * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM. +If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM, and you'll probably also need to install version 101 of WinVDIG from [here](http://shiffman.net/vdig/) so that Quicktime sees your camera. + +We're currently working on a proper cross-platform (Windows, Mac & Linux; 32 and 64 versions) capture system that will mean that no extra dependencies need to be installed. Currently we have no estimate as to when that might be ready however. An assembled JAR with all the required dependencies (except Quicktime) can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: </pre>Jonathon HareMon, 16 May 2011 08:22:58 -0000https://sourceforge.net308fbf92523bda046d6f2944801d202a6db5335dhttps://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v10 +++ v11 @@ -4,21 +4,23 @@ * The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option. - +If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option on OSX. On Windows, you'll need to be using a 32-bit JVM. + An assembled JAR with all the required dependencies (except Quicktime) can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: ~~~~~ :::bash -java -Xmx1G -d32 -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoSIFT +java -Xmx1G -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoSIFT ~~~~~ The second demo can be run with: ~~~~~ :::bash -java -Xmx1G -d32 -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoKLTSIFT -~~~~~ +java -Xmx1G -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoKLTSIFT +~~~~~ + +(Don't forget to add the -d32 option to the commands on a Mac!) Both demos operate in the same way; once loaded they display a live video picture. Hold the object you wish to track in front of the camera and press the spacebar to pause the video. You can then click on the video window to select the outline of the object you wish to track. Once the object is outlined, press the "c" key to capture the model, and then press the spacebar to resume the video. The object should then be tracked as you move it around. </pre>Jonathon HareFri, 13 May 2011 16:32:12 -0000https://sourceforge.net1cd2811dba171465faf31e40d56a478c82f63688https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v9 +++ v10 @@ -1,5 +1,26 @@ The OpenIMAJ difference-of-Gaussian/SIFT implementation is quite fast. To illustrate this, we have created two demos that illustrate SIFT extraction and matching (with homography fitting) in near real-time using a webcam as an input. -* The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). - -* The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=Bm5qUG-06V8). +* The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [here](http://www.youtube.com/watch?v=Bm5qUG-06V8). + +* The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). + +If you want to try these demos yourself you'll need to be on a Mac or Windows machine and have Quicktime installed, together with the `Quicktime4Java` libraries. You'll also need a Quicktime-compatible camera. As `Quicktime4Java` is a 32-bit library, you need to run the Java virtual machine in 32-bit mode by specifying the `-d32` option. + +An assembled JAR with all the required dependencies (except Quicktime) can downloaded [here](http://www.ecs.soton.ac.uk/~jsh2/openimaj/VideoSIFT.jar). To run the first demo, open a command prompt and navigate to the directory where you downloaded the JAR, and then run: + +~~~~~ +:::bash +java -Xmx1G -d32 -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoSIFT +~~~~~ + +The second demo can be run with: + +~~~~~ +:::bash +java -Xmx1G -d32 -cp VideoSIFT.jar org.openimaj.demos.video.videosift.VideoKLTSIFT +~~~~~ + +Both demos operate in the same way; once loaded they display a live video picture. Hold the object you wish to track in front of the camera and press the spacebar to pause the video. You can then click on the video window to select the outline of the object you wish to track. Once the object is outlined, press the "c" key to capture the model, and then press the spacebar to resume the video. The object should then be tracked as you move it around. + +The sourcecode for the demos is available in the subversion repository in the /trunk/demos/VideoSIFT maven project. + </pre>Jonathon HareFri, 13 May 2011 15:05:02 -0000https://sourceforge.netff22ce0ceacfb2620be7a3e7252d94a1caf6a825https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v8 +++ v9 @@ -1,5 +1,5 @@ The OpenIMAJ difference-of-Gaussian/SIFT implementation is quite fast. To illustrate this, we have created two demos that illustrate SIFT extraction and matching (with homography fitting) in near real-time using a webcam as an input. -* The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [http://www.youtube.com/watch?v=OHgkgN540KQ](here). +* The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [here](http://www.youtube.com/watch?v=OHgkgN540KQ). -* The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [http://www.youtube.com/watch?v=Bm5qUG-06V8](here). +* The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [here](http://www.youtube.com/watch?v=Bm5qUG-06V8). </pre>Jonathon HareFri, 13 May 2011 14:36:32 -0000https://sourceforge.net576e2e6b14febc61608646819cf5e95d398d3f84https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v7 +++ v8 @@ -1,0 +1,5 @@ +The OpenIMAJ difference-of-Gaussian/SIFT implementation is quite fast. To illustrate this, we have created two demos that illustrate SIFT extraction and matching (with homography fitting) in near real-time using a webcam as an input. + +* The first demonstration performs difference-of-Gaussian peak detection and SIFT extraction on every frame. The detected features are then matched against a model (selected by the user) and a homography is fitted. A video of the demo can be found [http://www.youtube.com/watch?v=OHgkgN540KQ](here). + +* The second demonstration performs uses a Kanade-Lucas-Tomasi tracker to track an object selected by the user. SIFT features are used to initialise the tracking window, and also to re-initialise it should be tracked object be lost from the scene. The advantage of this hybrid approach is that it is much more efficient than just using a pure SIFT based approach on every frame, and thus higher frame rates can be achieved. A video of the demo can be found [http://www.youtube.com/watch?v=Bm5qUG-06V8](here). </pre>Jonathon HareFri, 13 May 2011 14:35:34 -0000https://sourceforge.net1399f162f2805324190d83eb60b6302826f88210https://sourceforge.net/p/openimaj/wiki/VideoSIFT/<pre>--- v6 +++ v7 @@ -1,3 +1,0 @@ -&lt;APPLET archive="http://openimaj.sf.net/demos/VideoSIFT.jar" code="org.openimaj.demos.video.videosift.VideoSIFTApplet" width="300" height="300"&gt; -&lt;PARAM name="java_arguments" value="-d32 -Xmx1G"&gt; -&lt;/APPLET&gt; </pre>Jonathon HareFri, 06 May 2011 08:10:57 -0000https://sourceforge.netf7f94ebaf2cbe5a1a5a9d3d239ebfc4a939a3404