Latest results

As part of my hand tracking project I post these last videos. I believe I reached the top of the performance of the particle filter algorithm.

Unfortunately I want even better results so I should move to more complex algorithms. The problem is that real time performance is going to be much more difficult to achieve.

Mixing Histograms

In the last few days I implemented an adaptive skin classifier in make it easier for the tracking algorithms to find the objects. Emgu already has an adaptive skin classifier but you cannot change any parameters and the results are horrible.

Anyway, mixing two histograms in Emgu is not as straightforward as one might think.
The internal representation of the histograms is the N-th dimensional matrix class , MatND and the OpenCV does not overload any operators.

Thankfully there is a function in the cvInvoke that does the job.
I wasted about half an hour trying to use the cvAdd function but it crashed the whole thing about a dozen times, so I ended up using the cvAddWeighted function.

Below is the code snippet for histogram mixing :

        /// <summary>
        /// Mixes two histograms , new histogram is _mixCoeff * _histTarget + (1 - _mixCoeff) * _histMix
        /// Old target histogram is destroyed
        /// </summary>
        /// <param name="_histTarget">Target histogram</param>
        /// <param name="_hist1">Histogram 1</param>
        /// <param name="_hist2">Histogram 2</param>
        /// <param name="_mixCoeff">Mixing Coefficient</param>
        public static void MixHistograms(
            DenseHistogram _histTarget,
            DenseHistogram _hist1,
            DenseHistogram _hist2,
            float _mixCoeff)
        {
            CvInvoke.cvAddWeighted(
                _hist1.MatND.Ptr,
                _mixCoeff,
                _hist2.MatND.Ptr,
                1.0d - _mixCoeff,
                0,
                _histTarget.MatND.Ptr);
        }

PS. On a following post I will post the adaptive skin classifier.

Integral Histograms

In order to get good performance from the particle filter, I need at least 200 particles. Calculating so many histograms slows things down. So I will try to implement an integral histogram solution that will allow me to do fast calculations of histograms and thus speeding the whole thing.

After performing the transformation it is possible to extract a histogram with only 4 array lookups.

The paper I'm following is Integral Histogram: A Fast Way to Extract Histograms in Cartesian Spaces.