Human localization in video frames using a growing neural gas algorithm and fuzzy inference - Amosov, O.; Ivanov, Y.; Zhiganov, S. (Kompʹûternaâ optika, 2017)
A problem of human body localization in video frames using growing neural gas and feature description based on the Histograms of Oriented Gradients is solved. The original neuro-fuzzy model of growing neural gas for reinforcement learning (GNG-FIS) is used as a basis of the algorithm. A modification of the GNG-FIS algorithm using a two-pass training with fuzzy remarking of classes and building of a heat map is also proposed. As follows from the experiments, the index of the correct localizations of the developed classifier from 90.5% to 93.2%, depending on the conditions of the scene, that allows the use of the algorithm in real systems of situational video analytics.