Human Localization in the Video Stream Using the Algorithm Based on Growing Neural Gas and Fuzzy Inference

The problem of the human body localization in the video stream using the 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. The modification of GNG-FIS algorithm using 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 was 93%, that allows the use of the algorithm in real systems of situational video analytics.