Hardware-based solutions utilizing random forests for object recognition

Abstract

This paper presents how hardware-based machine learning models can be designed for the task of object recognition. The process is composed of automatic representation of objects as covariance matrices follow by a machine learning detector based on random forest (RF) that operate in on-line mode. We describe the architecture of our random forest (RF) classifier employing Logarithmic Number Systems (LNS), which is optimized towards a System-on-Chip (Soc) platform implementation. Results demonstrate that the proposed model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers, while allow fair comparisons between the precision requirements in LNS and of using traditional floating-point. © 2009 Springer Berlin Heidelberg.