Random forest-LNS architecture and vision

We describe an efficient architecture for generic object recognition system based on an ensemble classifier in a field programmable gate array (FPGA) environment. Utilization of a bag of covariance matrices as object descriptor improves the object recognition accuracy while speed up the learning process. We extend this technique, and present its hardware architecture, as well as object classifier based on on-line variant of random forest (RF) implemented using Logarithmic Number System (LNS). First, we describe the algorithmic and architecture of our model, comprises several computation modules. Then test and verified the model functionality using numerical simulation in the GRAZ02 dataset domain. It has been shown that the proposed system gained strong performance over floating-point and fixed-point precisions, even when only 10% of the training examples are used and is reasonably power efficient.