uTrack linear motion tracker pseudocode

• uTrack.m:
  • set up parameters
  • trackCloseGapKalmanSparse.m:
    • Input: read the input params and stored
    • Preamble:
      • read the coordinate data
      • calculate the nearest neighbor distance for each object
      • remove the empty start and end frames
    • Link frame-to-frame:
      sequence: link forward, time reverse, link backward, time reverse, link forward
      • linkFeaturesKalmanSparse.m:
        • Initilize input
        • Reserve Memory: allocate struct memory
        • Initial the first frame info for kalman initialize: stateVec, stateCor, noiseCor
          • first frame: kalmanInitialLinearMotion.m
          • others: use previous kalman info
        • Link frame by frame:
          • costMatLinearMotionLink2.m:
            • get Input
            • motion propagation
              • get the transfer func, 3 kind of potential TF for linear motion
              • For each objects in the 1st frame
                • get previous kalman info
                • compute new prediction
                • store in new kalman info and will be use for next time
            • calculate the cost matrix as the distance between prediction and actual measurement in 2rd frame
            • then findout the minimum cost for 3 kind of TF
            • use the user pre-defined max search radius to filter out some big distance (maxSearchRadius can also be defined by using local density)
            • set all distance in CostMat > searchRadius to NaN and square the costMat as the upperLeft sub-matrix of the final costMat
            • add lifetime penalty
            • birth and death cost is set to 1.05 * CostMat.max
            • finally construct the overall costMat as:
              • costMat=[costMat death;birth lrblock];
            • set all NaN in the costMat to -5
          • use the LAP algorithm to find best solution
          • then get the link index info:
            • indx1c: linked objects index in frame1
            • indx2c: linked objects index in frame2
            • indx1u: in 1st frame but not in 2rd frame
            • indx1c.count = indx2c.count
            • indx1c.count + indx1u.count = total objects count in 1st frame
          • then update the trackedFeatureInfo, near neighbor distance, prevCost matrixes
            • all this three will be stored as [indx1c indx2c;indx1u x]
          • update the feature life time, add 1 for miss linked objects, add 2 for linked objects
          • calc the Kalman gain, get the best prediction and update the kalman info for next prediction to use
          • rearrange all the tracks in the sequence of the start frame number
          • store the tracks' each objects' [x, y, z=0, amplitude, dx, dy, dz, damp]
      • Reverse the frame sequence and repeat the process of link backward
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    • Post processing:
      • getTrackSEL: get each track's start time and end time (frame number)
      • filter out those track life time < user pre-defined
      • re-calc the nndistance