Analysis and optimization of parameters used in training a cascade classifier
Abhishek Kumar Annamraju, Akash Deep Singh · Advances In Image and Video Processing · 2015
Training a cascade classifier for object detection using Local Binary Pattern (LBP) and Histogram of Gradients (HOG) features is computationally exorbitant. If the parameters of training are not chosen appropriately, the training may take weeks to complete with the output of an inefficient classifier. The state-of-the-art face recognition applications demand accurate and reliable cascade classifiers. Open Computer Vision (OpenCV) organization provides libraries which accomplish the training task once all parameters are given as inputs. In this paper we analyze the parameters experimentally and concluded with an optimal range of values for each of these parameters. Testing of the generated classifiers with optimal parameters values is performed on a dataset of 4000 test images. The training of these classifiers with optimal parameters takes an average training time of 25000 sec and provides average true positive detection of 88%.