Multimodal Machine Learning for Pedestrian Detection

Abstract

Designing and developing autonomous vehicles that are capable of moving safely on roads by sensing the environment has motivated researchers to focus on pedestrian detection systems so they can detect people as fast and accurately as possible. However, for pedestrian detection, it is crucial to consider not only the pedestrians themselves but their color as well, because color has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Therefore, considering skin color detection for implementing pedestrian detection systems is an essential required step in ensuring autonomous vehicles are further incorporated into our society. Detecting human skin has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, imaging conditions, and others. Recently it has been noted that pedestrian detection systems for autonomous vehicles perform poorly at detecting people with darker skin tones. Such findings indicate that there is a larger problem that is causing these issues: algorithmic bias. Algorithmic bias in pedestrian detection systems could be the leading factor of their poor performance due to the methods implemented and datasets used. Unfortunately, the algorithmic bias in this context has not been considered closely and it seems that many studies do not cover this aspect closely when discussing pedestrian detection systems for autonomous vehicles. To alleviate this, we attempt to explore different techniques that can be used to detect pedestrians while minimizing bias. In our experiment, we use both a YOLO v3 convolutional neural network and K-Means clustering for classifying skin-tones.