Introduction

HMAX aims to model hierarchical object recognition in cortex. I won’t get into details of HMAX model, just provide some useful links for curious readers:

http://riesenhuberlab.neuro.georgetown.edu/hmax.html

http://cbcl.mit.edu/cbcl/publications/index-pubs.html

My approach in using HMAX for currency recognition was pretty simple.

1. Generate proper С1-feature dictionary
2. Using C1-feature dictionary, generate C2 vectors for images in training dataset
3. Train multi-class SVM classifier using C2 vectors.
4. Test classifier on testing dataset
5. PROFIT

C1 dictionary generation
My initial approach was to generate dictionary automatically using an “interesting point” detector and then generate C-1 patches of size corresponding to the size of detected image structure. Hessian-Laplace (see Mikolajczyk K. and Schmid, C. 2004. ) method seemed to suit well for this task, since it detects characteristic scale of interesting points. My implementation of Hessian-Laplace worked reasonable fine, however number of interesting points it detected was big, so it was pretty hard for my C1 extractor to decide which one of these points was really “interesting” for currency recognition.
As a result – none of my automatically generated dictionaries (which contained about 1000 samples) produce suitable classification outputs.

So I decided to start from smaller dictionary, which would contain hand-picked, really descriptive patches from all kind of bills. For each image in C1 dataset I took 8 patches ( see example of pacthes below). All images in C1 dataset were resized to have width 300 pixels. Size of C1 dictionary now became 88.

Classifier training
The size of my training dataset was 337 images of different dollar bills of all classes (1 to 100) + set of “background” images.

I used LibSVM for classifier training and testing, using RBF and Linear classifier kernels.

To train models with RBF kernels I used script easy.py from LibSVM – this handy script automatically scales training data and the searches for the best C/gamma parameters of the kernel.
Linear classifiers were trained using semi-automated approach – I scaled training data first, then used grid.py to find best value for C parameter of the kernel.

Something like that:
svm-scale -s newdict.range newdict.l > newdict.scale

svm-train -t 0 -c 0.353553390593 newdict.scale newdict.model

And for testing:
svm-scale -r newdict.range newdict.t > newdict.t.scale

svm-predict newdict.t.scale newdict.model newdict.model.predict

Experiment results

Best classification accuracy I’ve reached so far is 88.62% using linear classifier.

Conclusions and future work

HMAX model has proved successful in classification of currency images and seem to have potential for better results.

Possible directions toward better classification could be:
1. Brightness correction. HMAX implementation I used seems to be pretty sensitive to brightness changes. I’ll need to investigate this more deeply and if necessary – normalize trainig/testing images to get rid of brightness-related issues.

2. Quality of C1-dictionary. While my current dictionary proved to be good enough to recognize dollar bill classes, I suppose it could be improved. For example – data there could be more patches per bill class, taken for different layers (bands) of C1 “pyramid”. The size of the features may also vary to achieve better results.

3. HMAX model tuning. Perhaps model modification according to approach of Jim Mutch и David G. Lowe ( PDF) would give better recognition results.

4. Use Ada-Boost in addition to SVM for better model training.

Build platform: Ubuntu 9.10
Target platform: Android

Download and prepare OpenCV library source code.

1. Download the latest version of OpenCV (http://sourceforge.net/project/showfiles.php?group_id=22870).

2. As build platform is Linux, select linux version (for example OpenCV2.1.0.tat.bz).

3. Unpack somewhere to home dir.

Download and prepare cross-compiler

1. Download Android NDK 3 for Linux (http://developer.android.com/sdk/ndk/index.html)

2. Unpack it to ~/android_ndk_3/

3. Then run ~/android_ndk_3/build/host-setup.sh but first fix the error in line 119

Change

if [ "$result" = "Pass" ] ; then

to

if [ "$result" == "Pass" ] ; then

4. Do/install whatever needed to let host-setup.sh complete successful.

Create NDK project/Modify Makefiles

There is one big issue with NKD toolchein. It has trimmed stdc library which does not contain STL. Because of that some files (like cvkdtree.cpp in cv) can not be compiled since they use vector, list and other stuff. The solution is to compile STL from source code. In my OpenCV NDK project I used STL sources from uClibc (http://www.uclibc.org).

The simpliest way to start your OpenCV NDK project is to update hello-jni project with OpenCV source files.

The ~/android_ndk_3/apps/hello-jni/project/jni folder of hello-jni project may look like this

cv
- hdr
- src
cvaux
- hdr
- src
cxcore
- hdr
- src
stl
- hdr
- src
Android.mk
hello-jni.c

The ~/android_ndk_3/apps/hello-jni/project/jni/Android.mk may looks like this

APPS_PATH := $(call my-dir)############################
# stl
############################
include $(CLEAR_VARS)LOCAL_PATH := $(APPS_PATH)/stl/src
LOCAL_C_INCLUDES := $(APPS_PATH)/stl/hdrLOCAL_MODULE := stl
LOCAL_SRC_FILES := string.cpp algorithm.cpp char_traits.cpp iterator.cpp limits.cpp list.cpp vector.cppinclude $(BUILD_STATIC_LIBRARY)############################
# cxcore
############################
include $(CLEAR_VARS)LOCAL_PATH := $(APPS_PATH)/cxcore/src
LOCAL_C_INCLUDES := $(APPS_PATH)/cxcore/hdrLOCAL_CXXFLAGS := -DHAVE_CONFIG_HLOCAL_MODULE := cxcore
LOCAL_SRC_FILES := cxalloc.cpp cxarithm.cpp cxarray.cpp cxcmp.cpp cxconvert.cpp cxcopy.cpp cxdatastructs.cpp cxdrawing.cpp cxdxt.cpp cxerror.cpp cximage.cpp cxjacobieigens.cpp cxlogic.cpp cxlut.cpp cxmathfuncs.cpp cxmatmul.cpp cxmatrix.cpp cxmean.cpp cxmeansdv.cpp cxminmaxloc.cpp cxnorm.cpp cxouttext.cpp cxpersistence.cpp cxprecomp.cpp cxrand.cpp cxsumpixels.cpp cxsvd.cpp cxswitcher.cpp cxtables.cpp cxutils.cpp dummy.cppinclude $(BUILD_STATIC_LIBRARY)############################
# cv
############################
include $(CLEAR_VARS)

LOCAL_PATH := $(APPS_PATH)/cv/src
LOCAL_C_INCLUDES := $(APPS_PATH)/cv/hdr $(APPS_PATH)/cxcore/hdr $(APPS_PATH)/stl/hdr

LOCAL_MODULE := cv
LOCAL_SRC_FILES := cvkdtree.cpp cvaccum.cpp cvadapthresh.cpp cvapprox.cpp cvcalccontrasthistogram.cpp cvcalcimagehomography.cpp cvcalibinit.cpp cvcalibration.cpp cvcamshift.cpp cvcanny.cpp cvcolor.cpp cvcondens.cpp cvcontours.cpp cvcontourtree.cpp cvconvhull.cpp cvcorner.cpp cvcornersubpix.cpp cvderiv.cpp cvdistransform.cpp cvdominants.cpp cvemd.cpp cvfeatureselect.cpp cvfilter.cpp cvfloodfill.cpp cvfundam.cpp cvgeometry.cpp cvhaar.cpp cvhistogram.cpp cvhough.cpp cvimgwarp.cpp cvinpaint.cpp cvkalman.cpp cvlinefit.cpp cvlkpyramid.cpp cvmatchcontours.cpp cvmoments.cpp cvmorph.cpp cvmotempl.cpp cvoptflowbm.cpp cvoptflowhs.cpp cvoptflowlk.cpp cvpgh.cpp cvposit.cpp cvprecomp.cpp cvpyramids.cpp cvpyrsegmentation.cpp cvrotcalipers.cpp cvsamplers.cpp cvsegmentation.cpp cvshapedescr.cpp cvsmooth.cpp cvsnakes.cpp cvstereobm.cpp cvstereogc.cpp cvsubdivision2d.cpp cvsumpixels.cpp cvsurf.cpp cvswitcher.cpp cvtables.cpp cvtemplmatch.cpp cvthresh.cpp cvundistort.cpp cvutils.cpp dummy.cpp

LOCAL_STATIC_LIBRARIES := cxcore stl

include $(BUILD_STATIC_LIBRARY)

############################
# cvaux
############################
include $(CLEAR_VARS)

LOCAL_PATH := $(APPS_PATH)/cvaux/src
LOCAL_C_INCLUDES := $(APPS_PATH)/cvaux/hdr $(APPS_PATH)/cv/hdr $(APPS_PATH)/cv/src $(APPS_PATH)/cxcore/hdr $(APPS_PATH)/stl/hdr

LOCAL_MODULE := cvaux
LOCAL_SRC_FILES := camshift.cpp cvaux.cpp cvauxutils.cpp cvbgfg_acmmm2003.cpp cvbgfg_codebook.cpp cvbgfg_common.cpp cvbgfg_gaussmix.cpp cvcalibfilter.cpp cvclique.cpp cvcorrespond.cpp cvcorrimages.cpp cvcreatehandmask.cpp cvdpstereo.cpp cveigenobjects.cpp cvepilines.cpp cvface.cpp cvfacedetection.cpp cvfacetemplate.cpp cvfindface.cpp cvfindhandregion.cpp cvhmm.cpp cvhmm1d.cpp cvhmmobs.cpp cvlcm.cpp cvlee.cpp cvlevmar.cpp cvlevmarprojbandle.cpp cvlevmartrif.cpp cvlines.cpp cvlmeds.cpp cvmat.cpp cvmorphcontours.cpp cvmorphing.cpp cvprewarp.cpp cvscanlines.cpp cvsegment.cpp cvsubdiv2.cpp cvtexture.cpp cvtrifocal.cpp cvvecfacetracking.cpp cvvideo.cpp decomppoly.cpp dummy.cpp enmin.cpp extendededges.cpp precomp.cpp vs/bgfg_estimation.cpp vs/blobtrackanalysis.cpp vs/blobtrackanalysishist.cpp vs/blobtrackanalysisior.cpp vs/blobtrackanalysistrackdist.cpp vs/blobtrackgen1.cpp vs/blobtrackgenyml.cpp vs/blobtrackingauto.cpp vs/blobtrackingcc.cpp vs/blobtrackingccwithcr.cpp vs/blobtrackingkalman.cpp vs/blobtrackinglist.cpp vs/blobtrackingmsfg.cpp vs/blobtrackingmsfgs.cpp vs/blobtrackpostprockalman.cpp vs/blobtrackpostproclinear.cpp vs/blobtrackpostproclist.cpp vs/enteringblobdetection.cpp vs/enteringblobdetectionreal.cpp vs/testseq.cpp

# failed to compile
#cv3dtracker.cpp

LOCAL_STATIC_LIBRARIES := cv cxcore stl

include $(BUILD_STATIC_LIBRARY)

The ~/android_ndk_3/apps/hello-jni/Application.mk file needs to be updated as follows

APP_PROJECT_PATH := $(call my-dir)/project
APP_MODULES      := stl cxcore cv cvaux hello-jni

To build the project go to ~/android_ndk_3 and type

make APP=hello-jni

Of course there will be compile issues. Understand and fix them. Easiest cases are related to syntax mismatch between different compilers. In more complicated cases some code should be commented out. For example usage of libs with optimizations for Intel processor is not needed for ARM.

HighGui is also can be built but only partially. Simply remove files that causing problems from Android.mk. In my case the rest of files were enough to use cvLoadImage function for bmp file.

Running facedetect openCV example

There is no way to run native C code as separate application on Android. Instead native C functions can be called from Java apps. Because of that I made native function FaceDetect using OpenCV example application facedetect.c.

The declaration looks like this

void
Java_com_example_hellojni_HelloJni_FaceDetect( JNIEnv* env, jobject thiz, jbyteArray jyuv_buff, int w, int h, jbyteArray jbgra_buff)
{
…
}

This function takes YUV_NV21 buffer (preview from camera captured by Java app), converts it to BGRA8888, searches the faces, draws circles around the faces and returns updated RGBA8888 buffer back to Java app. Java app can draw it on the screen.

people_counter.avi

Marketing researches are area where required to analyze a lot of data. E.g. we want to understand how many people are visiting a bank. In order to count this value, we need to count each man or woman which are entering to or exiting from the bank. For resolving this task there are a lot of approaches: e.g. use special gate with laser or mechanical counter. Though there are people counting tasks where such approaches cannot work or too unuseful. E.g. barrier cannot be used where people flow is very high, and laser counters have limitations as well.

Opposite the approaches above, we found papers where top-mounted camera is used for resolving the people counting task.

 The fact is that most of organization have own IP or CCTV camera based security infrastructure. And also often there is a camera which is already top-mounted. Thus top-mounted camera counting approach is looked very perspective from reusing infrastructure point of view.

We researched a lot of approaches. There are a lot of ready methods for people counting with top-mounted camera. But such methods either are patented or don’t meet our expectation in quality or speed. Thus we developed own method (see demo video).

Method counts human each time they cross a predefined counting line (that is why it is often called as “line-crossing”). The assumption is that the line should be selected orthogonally to the main people flow.

Now we skip implementation details of the method. If shortly, our method is a real time 15 fps+, we tested it usual USB or IP cameras. We tested our method in indoor and outdoor use-case and the quality is 80-90% (dependent on environment condition).

Our method can be used in other applications, e.g. vehicle counting, you could look at the video how it works. Have a nice watching…

tools.avi

This object recognition algorithm is based on own pattern-matching algorithm. The algorithm is able to recognize pre-trained objects which are defined with special set of templates. Theoretically, the algorithm works with any “3D” objects which have good projection on 2D coordinates. However, natural 3D objects are covered by few templates for set of 2D projections. For limited number of object templates, the algorithm works in real-time on PC (Intel P4 3.0 GHz) .

The currency recognition demo application works under Windows XP, Intel P4 3GHz. Quality of recognition: 85%. The solution is cross-platform. The application was tested on Linux, ARM11 and on Linux/Windows, Intel Atom.

Interesting details can be found at: http://www.computer-vision-software.com/blog/2009/10/barcode

Positive images

Why positive images are named so?

Because a positive image contains the target object which you want machine to detect. Unlike them, a negative image doesn’t contain such target objects.

What’s vec file in OpenCV haartraining?

During haartraining positive samples should have the same width and height as you define in command “-w -h size”.  So original positive images are resized  and packed as thumbs to vec file. Vec file has header: number of positive samples, width, height and contain positive thumbs in body.

Is it possible to merge vec files?

Yes, use Google, there are free tools, written by OpenCV’s community.

I have  positive images, how create vec file of positive samples?

There is tool in C:\Program Files\OpenCV\apps\HaarTraining\src createsamples.cpp.  Usage:

createsamples -info positive_description.txt -vec samples.vec -w 20 -h 20

What’s positive description file?

The matter is that, on each positive image, there can be several objects. They have bounding rectangles: x,y, width, height.  So you can write such description info of image:

positive_image_name  num_of_objects x y width height x y width height …

Text file, which contains such info about positive images is called description file. So during vec file generation,  really objects are packed, but not whole image. Essentially vec file is needed to speed up machine learning.

Do I always need description file, even if I have only one object on a image?

Yes, with createsamples you need description file.  If you have only one object, it’s bounding rectangle may be bounding rectangle of whole image. If you want, write your own tool for vec file generation =)

Should lightning conditions and background be various on positive images?

Yes, it’s very important. On each positive image, beside object, there is background. Try to fill this background with random noise, avoid constant background.

How much background should be on positive image?

If you have much background pixels on your positive images in comparison with object’s pixels – it’s bad since the haartraining could remember the background as feature of positive image.

If you don’t have background pixels at all – it’s also bad. There should be small background frame on positive image

Should all original positive images have the same size?

No, original images can have any size.  But it’s important that width, height of this rectangle have the same aspect ratio as -w -h.

What’ s  -w and -h should I put in createsamples? Should it be always square?

You can put any value to -w and -h depend on aspect ratio of the target object which you want to detect.  But objects of smaller size will not be detected! For faces, commonly used values are 24×24, 20×20. But you may use 24×20, 20×24, etc.

Errors during vec file generation: Incorrect size of input array, 0 kb vec file,

-First check you description file: positive_image_name should be absolute path name without spaces like “C:\content\image.jpg” not “C:\con tent\image.jpg” or relative path name.

-Avoid empty lines in description file

-Resolution of original positive image file should be not less, then -w -h parameters you put.

-Check that positive images are available in your file systems and not corrupted.

-There can be unsupported formats. Jpeg, Bmp, PPM are supported!

Example of vec file generation!

Let’s working directory be C:\haartraining. In it there is createsamples.exe. There is folder

C:\haartraining\positives. So create description file positive_desc.txt.

positives\image1.jpg 1 10 10 20 20

positives\image2.jpg 2 30 30 50 50 60 60 70 70

or

C:\haartraining\positives\image1.jpg 1 10 10 20 20

C:\haartraining\positives\image2.jpg 2 30 30 50 50 60 60 70 70

You should avoid empty lines and empty space in image’s path

createsamples -info positive_desc.txt -vec samples.vec -w 20 -h 20

Negative images

What negative images should I take?

You can use any image of OpenCV supported formats, which does not contain target objects (which are present on positive images). But they should be various – it’s important! Good enough database is here

Should negative images have the same size?

No. But the size should not be less than -w -h, which were put during vec file generation.

What’s description file for negative image?

It’s just text file, often called negative.dat, which contains full path to negative images like:

image_name1.jpg

image_name2.jpg

Avoid empty lines in it.

How many negative/positive image should I take?

It depends on your task.  For real cascades there should be about 1000 positive images and 2000 negative images e.g.

Good enough proportion is  positive:negative = 1:2, but it’s not hard rule! I would recommend first to use small number of samples, generate cascade, test it, then enlarge number of samples.

Launch haartraining.exe (OpenCV\apps\HaarTraining\src)

Example of launching

Working directory is C:\haartraining with haartraining.exe tool and samples.vec file.

Let’s negative images are in C:\haartraining\negative, in this case negative.dat should be like this:

negative\neg1.jpg

negative\neg2.jpg

…

So in C:\haartraining launch this:  haartraining -data haarcascade -vec samples.vec -bg negatives.dat -nstages 20  -minhitrate 0.999 -maxfalsealarm 0.5 -npos 1000 -nneg 2000 -w 20 -h 20 -nonsym -mem 1024

• w  h  is the same, you put during vec file generation
• npos nneg  – number of positive samples and negative samples
• mem – RAM memory, that program may use
• maxfalsealarm – maximum false alarm, that stage may have. If big false alarm – it could be bad detection system
  
• minhitrate – minimal hit rate, that should stage have at least
  
• nstage – number of stages in cascade

What’ s falsealarm and hitrate of stage?

You should read theory of adaboost about strong classifier. Stage is strong classifier. In short:

• For example you have 1000 positive samples. You want your system to detect 900 of them. So desired hitrate = 900/1000 = 0.9. Commonly, put minhitrate = 0.999
• For example you have 1000 negative samples. Because it’s negative, you don’t want your system to detect them. But your system, because it has error, will detect some of them. Let error be about 490 samples, so false alarm = 490/1000 = 0.49. Commonly,put false alarm  = 0.5

Are falsealarm and hitrate depend on each other?

Yes, there is dependency. You could not put minhitrate = 1.0 and maxfalsealarm = 0.0. .

Firstly, the system builds classifier with desired hitrate, then it will calculate it’s falsealarm, if the false alarm is higher than maxfalsealarm, the system will reject such classifier and will build the next one. During haartraining you may see such:

N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+—-+—-+-+———+———+———+———+
| 0 |25%|-|-1423.312590| 1.000000| 1.000000| 0.876272|

HR – hitrate

FA – falsealarm

What’s falsealarm and hitrate of whole cascade?

Cascade is linked list (or three) of stages. That’s why:

• False alarm of cascade = false alarm of  stage 1* false alarm of  stage 2* …
• Hit rate = hitrate of  stage 1 * hitrate of stage 2* …

How many stages should be used?

• If you set big number of stages, then you will achieve better false alarm, but it will take more time for generating cascade.
• If you set big number of stages, then the detection time could be slower
• If you set big number of stages, then the worse hitrate will be (0.99*0.99*… etc). Commonly 14-25 stages are enough
• It’s useless to set many stage, if you have small number of positive, negative samples

What’s weighttrimming, eqw, bt, nonsym options?

Really all these parameters are related to Adaboost, read theory. In short:

• nonsym – If you positive samples are not X or Y symmetric, put -nonsym, -sym is default!
• eqw – if you have different number of pos and neg images, it’s better to put no eqw
• weighttrimming – for calculation optimization. It can reduce calculation time a little, but quality may be worse
• bt – what Adaboost algorithm to use: Real AB, Gentle AB, etc.

What’s  minpos, nsplits, maxtreesplits options?

These parameters are related to clustering. In Adaboost different week classifier may be used: stump-based or tree-based.  If you choose nsplits > 0, tree-based will be used and you should set up minpos and maxtreesplits.

• nsplits – minimun number of nodes in tree
• maxtreesplits – maximum number of nodes in tree. If maxtreesplits < nsplits,  tree will not be built
• minpos – number of positive images, that can be used by one node during training.  All positive images are splitted between nodes. Generally minpos  should be not less than  npos/nsplits.

Errors and stranges during haartraining!

• Error (valid only for Discrete and Real AdaBoost): misclass – it’s warning, but no error.  Some options are specific to D and R Adaboost.  So your haartraining is ok.
• Screen is filled with such | 1000 |25%|-|-1423.312590| 1.000000| 1.000000| 0.876272| – your training is cycled, restart it. First column should have value < 100
• cvAlloc fails. Our of memory – you give too much negative images or sample.vec is too big. All these pictures are loaded to RAM.
• Pay attention you put the same -w and -h, as during vec file generation
• Pay attention, that number of positive samples and negative samples, you put in -npos -nneg are really available
• Avoid empty line in negative.dat file
• Required leaf false alarm rate achieved. Branch training terminated – it’s impossible to build classifier with good false alarm on this negative images. Check your negative images are really negative =),  maxfalsealarm should be in [0.4-0.5]

OpenCV XML haarcascade

During haartraining, there are txt file in haarcascade folder, how can we get XML  from them?

There is OpenCV/samples/c/convert_cascade.c. Use like:

convert_cascade –size=”20×20″ haarcascade haarcascade.xml

How can I test generated XML cascade?

 There is OpenCv/apps/HaarTraining/src /perfomance.cpp. You need have positive images(not used during training) and positive description file.  Use like:

performance -data haarcascade -w 20 -h 20 -info positive_description.txt -ni

performance -data haarcascade.xml -info positive_description.txt -ni

Time and Speed of haar cascade generation

Average time to generate cascade on PC?

It depends on task and your machine.  I generated cascade for face detection, for this used such parameters: -nstages 20 -minhitrate 0.999 -maxfalsealarm 0.5 -npos 4000 -nneg 5000 -w 20 -h 20 -nonsym -mem 1024.  It took 6 days on Pentium 2.7GHZ 2GB RAM.

What is OpenMP?

“The OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C, C++ and Fortran on many architectures, including Unix and Microsoft Windows platforms“. If you have MT processor, you can use it.  In code you should add OpenMP defines and put compile options.  For example in VisualStudio2005: Properties->C/C++->Language->OpenMP support

Is it possible to improve speed of haartraining?

Yes, one of possible ways is to use parallel programming. We have realized OpenCV haartraining using MPI for linux cluster. You can read it here

Object detection with OpenCV XML cascades

Is it possible to detect rotated faces?

Yes. It is impossible to generate cascade, which can detect face in all orientations. But you can generate cascade for each orientation separately. For this you need positive content of rotated faces. You can try to generate cascade with OpenCV , add -mode ALL, with it tilted haar feature will be used. But it’s badly implemented, at least in OpenCV 1.1. If you want you can add your own feature to opencv haartraining – it’s not too hard.

Another approach is to write head pose estimator. Then rotate your pictures, so that you have frontal face and detect it with OpenCV default face cascade

Is it possible to recognize gender, attention, race with  Haar features?

We tried, but could not do it with OpenCV haartraining. That’s why for such classification, we used our own gender and attention classificators. Of course you can use Adaboost for this task, which is implemented in haartraining, but we did not get good results.

Is it possible to detect faces in real time?

Yes.  On PC default OpenCV facedetector takes about 200 ms for 640×480 picture, about 5fps – it’s not real time. We have changed facedetector and get about 15 fps – which is real time. You can see results  here and here

We started our work with attempts to understand, what attention we want to detect.  On the one hand, It seems very easy  to say, if person has attention or not.  On the other hand,  it’s very difficult to formalize: What attention is.  In some articles, it’s considered to detect attention based on eyes information.  But if person wears sunglasses? Another “criteria” is to to use nose information: Where nose points at! For our business case nose information is not enough either. More over, nose can also be “hidden”.

That’s why our attention is based on head pose information. We collected face images, which in our opinion, have attention and don’t have attention.  To be honest, most of images with attention were frontal faces and vice versa.  These two sets resolve task.

To teach machine to detect attention , we need a machine learning algorithm. We have Viola Jones one and if it can detect face/non face, why not use it to detect attention/non attention? Learning samples we have… So with Adabost, we chose 100 Haar-like features. With them, each image is converted to 100-dimensional vector.  To classify it, we used C4.5.  Self-test was very good: 97% accuracy.  But when started testing on real video, we had bad result: 60% accuracy. The problems begun, when  lightning conditions were modified or face shifted some pixels, even despite the fact that, we used normalization like in OpenCV Viola Jones algorithm. The matter is that, face and non-face images are very different, but faces with attention and with non-attention are very similar.

Thus, we needed lightning-invariant method, which is not so sensitive to XY-shifting.  We developed our own template-matching method.  First, using PCA, we get templates of face. With these templates, each face is  converted to N-dimensional vector, which is classified with SVM. Accuracy of our attention system is about 90%.  Its working you can see in our Audience Measurement system.

nike.avi

This is a demo video of the invariant orientation and scale fast object detection algorithm. The algorithm is a robust in cases when the object is deformed a little

The algorithm is a cross-platform solution.

Performance:

• on ARM11 530MHz, the algorithm gives 1 fps for 640×480 frame;
• on Intel P4 3Hz, the algorithm gives 12 fps and more for 640×480 frame.

Quality: 86%.

This is demo application for Rhonda barcode recognition library. It’s cross-platform library written on C++ language. It was tested on ARM Cortex-A8, ARM11 and x86 platforms.

Average recognition time on 640×480 frames:

P4 – 3GHz:  25ms

Program description:

main window:

Supported barcode’s types: EAN13, UPCA

SYSTEM REQUIREMENTS:
Recommended PC
Processor: Intel P4
Memory: 1GB RAM
Operating system: Windows XP SP3
Minimal screen resolution for Barcode Demo is 1024*768

Recommended camera
Barcode Demo works with any standard USB camera.
Web cam must support 640×480 and 960×720 resolutions and focus range should start  from 5 cm (2 inch).
If you do not already have a camera, we recommend that you purchase the Logitec QuickCam PRO 9000 or the Logitec QuickCam Sphere AF

USAGE INSTRUCTION:
Attach camera to PC and then run “rhonda_barcode_demo.exe”  .

Demo application supports 2 modes: 640×480 and 960×720 resolutions.

Recomended distance from camera to barcode:
5-11 cm (2-4 inch)  for 640×480 mode
5-20 cm (2-8 inch)  for 960×720 mode

Each frame is scanned in 8 directions.  Time of processing of each frame and  each direction are printed to a console window.

For better recognition, use manual focus of your camera.

Press link below to download the demo:

1604 Downloads Since 2009-09-28