Here is a first attempt to do what the diagram shows.
Things of interest :

• face detect is very very slow - so its great to hang off of the trackingCamera stack - as it will never re-process the same frame
• the detect stack varies greatly - filterContours within it takes a proportional amoun tof time depending on the number of contours it needs to outline - this filter has a lot of potential to tweak - some variables include the minimum or maximum number of pixels to count as a blob, also Alessandruino and I were talking about finding the average location of all blobs found, which would represent the "center" of activity
• templateMatch is WAY to quick - its fps is 90 - while the camera feed is only 30 fps - this means it processing 60 fps twice :P
• templlate is very quick, but I'm interested in saving a large number of templates in memory for more robust matching - this should possibly slow it down to a reasonable rate
• I noticed when I stopped the capture, I could not start it again - this needs to be fixed
• simple non-blocking references of video sources was an easy implementation, but perhaps not the best .. I'm still considering blocking pipelines, so that only "new" frames are processed 
• overall it worked ok - lots of potential - but there needs to be a centralized system (Cortex) which tweaks, starts, stops, and changes these stacks depending on need
• the code I posted in the web page (vs checked in), because I consider it only a "test" and likely - it will not work as the system evolves..
• there needs to be a new filter which takes a bounding box or contours and creates "cut-out" tempates to be used later
• bug in displaying pipeline details when done in code back to the gui
• match template needs to accept an arraylist or map of images to  match, its result should be matched integer values assigned to each image
• capture stopCapture needs to work as expected

LoggingFactory.getInstance().configure();

LoggingFactory.getInstance().setLevel(Level.WARN);

 

 

OpenCV trackingCamera = (OpenCV) Runtime.createAndStart("trackingCamera", "OpenCV");

OpenCV detector = (OpenCV) Runtime.createAndStart("detector", "OpenCV");

OpenCV faceDetect = (OpenCV) Runtime.createAndStart("faceDetect", "OpenCV");

OpenCV templateMatch = (OpenCV) Runtime.createAndStart("templateMatch", "OpenCV");

 

// filters begin -----------------

 

trackingCamera.addFilter(new OpenCVFilterPyramidDown("pyramidDown"));

trackingCamera.addFilter(new OpenCVFilterGray("gray"));

trackingCamera.addFilter(new OpenCVFilterLKOpticalTrack("lk"));

 

detector.addFilter(new OpenCVFilterDetector("detector"));

detector.addFilter(new OpenCVFilterErode("erode"));

detector.addFilter(new OpenCVFilterDilate("dilate"));

detector.addFilter(new OpenCVFilterFindContours("findContours"));

 

faceDetect.addFilter(new OpenCVFilterFaceDetect("faceDetect"));

 

templateMatch.addFilter(new OpenCVFilterMatchTemplate("matchTemplate"));

 

// filters end -----------------

 

GUIService gui = new GUIService("opencv_gui");

gui.startService();

gui.display();

 

// setup pipelines

faceDetect.setInpurtSource("pipeline");

faceDetect.setPipeline("trackingCamera.gray");

 

detector.setInpurtSource("pipeline");

detector.setPipeline("trackingCamera.gray");

 

templateMatch.setInpurtSource("pipeline");

templateMatch.setPipeline("faceDetect.faceDetect");

 

trackingCamera.capture();

faceDetect.capture();

detector.capture();

templateMatch.capture();

 

trackingCamera.broadcastState();

faceDetect.broadcastState();

detector.broadcastState();

templateMatch.broadcastState();