压缩跟踪Compressive Tracking源码理解-赢咖4注册

aihot 2017-04-27 23:51:15 OpenCV | 查看评论

void CompressiveTracker::processFrame(Mat& _frame, Rect& _objectBox)

{

// predict

//在上一帧跟踪到的boxbox周围，采集需要检测的box框

sampleRect(_frame, _objectBox, rSearchWindow, detectBox);

//计算这一帧的积分图

integral(_frame, imageIntegral, CV_32F);

//用积分图来计算上面采集到的每个box的harr特征

getFeatureValue(imageIntegral, detectBox, detectFeatureValue);

int radioMaxIndex;

float radioMax;

//对上面的每个box进行匹配分类

radioClassifier(muPositive, sigmaPositive, muNegative, sigmaNegative, detectFeatureValue, radioMax, radioMaxIndex);

//得到分数最高的那个目标box

_objectBox = detectBox[radioMaxIndex];

// update

//在新跟踪到的这个目标box的周围，采集正样本和负样本来更新我们的分类器

sampleRect(_frame, _objectBox, rOuterPositive, 0.0, 1000000, samplePositiveBox);

sampleRect(_frame, _objectBox, rSearchWindow*1.5, rOuterPositive+4.0, 100, sampleNegativeBox);

//通过上面的积分图，计算我们采样到的正负样本的box的harr特征

getFeatureValue(imageIntegral, samplePositiveBox, samplePositiveFeatureValue);

getFeatureValue(imageIntegral, sampleNegativeBox, sampleNegativeFeatureValue);

//通过上面的正负样本的特征来更新我们的分类器

classifierUpdate(samplePositiveFeatureValue, muPositive, sigmaPositive, learnRate);

classifierUpdate(sampleNegativeFeatureValue, muNegative, sigmaNegative, learnRate);

}