An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images

TL;DR

This paper introduces a new entropy-based outlier score embedded in graph-based dimensionality reduction, effectively identifying novel road infrastructure images and outperforming existing methods in real-world datasets.

Contribution

The work presents a novel unsupervised outlier score using entropy of similarities, integrated with graph-based dimensionality reduction, and applies it to infrastructure image novelty detection.

Findings

High potential in identifying infrastructure outliers

Outperforms state-of-the-art methods in accuracy

Generalizes well across diverse datasets

Abstract

A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neighbor graphs of those techniques. Hence, the same measure of similarity that is used to project the data into lower dimensions, is also utilized to determine the outlier score. The outlier score is realized through a weighted normalized entropy of the similarities. This score is applied to road infrastructure images. The aim is to identify newly observed infrastructures given a pre-collected base dataset. Detecting unknown scenarios is a key for accelerated validation of autonomous vehicles. The results show the high potential of the proposed technique. To validate the generalization capabilities of the outlier score, it is additionally applied to various real world datasets. The overall average performance in identifying outliers using the proposed methods is higher compared to state-of-the-art methods. In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work. This tool and the implementation of the entropy based outlier score in combination with Uniform Manifold Approximation and Projection are made publicly available.