Machine Learning Approaches on Pedestrian Detection in an autonomous vehicle

• Published in: 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) pp. 1 - 8
• Main Authors: Ranganayaki, V., J, Jency Rubia, Ramesh, P.S., Rammohan, K, Lincy, R.Babitha, Deepak, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22-02-2023
• Subjects: Convolutional Neural Network (CNN); Image color analysis; k-Nearest Neighbors (KNN); Machine learning; Naïve Bayes Classifiers (NBC); Neural networks; Pedestrians; Predictive models; Roads; Support vector machines
• DOI: 10.1109/ICECCT56650.2023.10179836

In autonomous driving, detecting pedestrians is a safety-critical activity, and the decision to avoid a person must be made as quickly as possible with as little delay as possible. In this work, INRIA and PETA datasets are taken. The progression of the work that is being proposed is broken up into three phases. The first step is to detect edges, the second step is to group colours, and the third step is extracting the feature, which includes screening body parts of pedestrians and detecting shoulder lines. The machine learning classifiers such as SVM, Naïve Bayes and KNN are taken for predicting the pedestrian in the road. The accuracy for SVM, Naïve Bayes and KNN are calculated as 93.58, 94.42 and 98.44 respectively. With the KNN model, it achieves the highest accuracy for predicting the exact images.