Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception

Autor:	Melotti, Gledson, Bastos, Johann J. S., da Silva, Bruno L. S., Zanotelli, Tiago, Premebida, Cristiano
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approaches, with the intention of reducing the false positive rate (FPR). The reduction of the FPR becomes increasingly important in perception systems since the misclassification of an object can potentially cause accidents. In particular, this work presents a strategy through Bayesian inference to reduce the FPR considering the likelihood function as a cumulative distribution function from Gaussian kernel density estimations, and the prior probabilities as cumulative functions of normalized histograms. The validation of the proposed methodology is performed on the KITTI dataset using deep networks (DenseNet, NasNet, and EfficientNet), and recent 3D point cloud networks (PointNet, and PintNet++), by considering three object-categories (cars, cyclists, pedestrians) and the RGB and LiDAR sensor modalities. Comment: This paper has been submitted to the journal Pattern Recognition Letters
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.05951 Zobrazit plný text záznamu View this record from Arxiv