Binary object recognition system on FPGA with bSOM

This paper introduces the implementation of an FPGA-based tri-state rule binary Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, with a node labelling algorithm which makes it capable of object classification. The bSOM is used for appearance-based object identification during tracking in video sequences. It is designed to provide part of an end-to-end surveillance system implemented wholly on FPGA. It is trained off-line using a labelled training data set for nine objects, using binary signatures extracted from the colour histogram, and successfully used for appearance-based identification of objects in approximately 85% of cases in a fairly challenging data set. The paper identifies how this preliminary work can be extended to provide full on-line appearance-based identification and tracking.