Enhanced target recognition using quadratic correlation filter networks

Quadratic Correlation Filters (QCFs) have been shown to be useful at separating target areas of interest from background clutter for two-class discrimination. However, extension to multi-class discrimination is not straight-forward, as QCFs primarily maximize the distance of the response of a filtered target area from clutter. Several attempts have been made to extend QCFs to multi-class discrimination using support vector machines and convolutional neural networks. In addition, detection and recognition of targets that are considered \unresolved" are still elusive for neural network architectures like YOLO which have minimum target size requirements. This work will show that the localization aspect of a QCF neural network layer paired with feature extraction layers of a purely convolutional neural network provides a robust solution to this problem. We will compare the recognition accuracy to YOLO outputs, since this is the current state-of-the-art for target localization and recognition for autonomous vehicles.