Usually one makes an image scale pyramid and then only scans with the 70x100 windows across all scales i.e. as in opencv HOGDescriptor:
double scale = 1.;
double scale0 = 1.05;
int maxLevels = 64;
int nLevels;
Size templateSize(70,100);
cv::Mat testImage = cv::imread("test1.jpg");
vector<double> levelScale;
for( nLevels = 0; nLevels < maxLevels; nLevels++ )
{
levelScale.push_back(scale);
if( cvRound(testImage.cols/scale) < templateSize.width ||
cvRound(testImage.rows/scale) < templateSize.height ||
scale0 <= 1 )
break;
scale *= scale0;
}
nLevels = std::max(nLevels, 1);
levelScale.resize(nLevels);
int level;
for(level =0; level<nLevels; level++)
{
cv::Mat testAtScale;
Size sz(cvRound(testImage.cols/levelScale[level]),
cvRound(testImage.rows/levelScale[level]));
resize(testImage,testAtScale,sz);
//result = match(template,testAtScale);
//cv::imshow("sclale",testAtScale);
//cv::waitKey();
}
you would then need to post-process your results back to the original scale, this is simple with a box, but if you have a heat map / response map / probability map, then re-sizing it back up maybe somewhat hacky.