WITHDRAWN: Review on intelligent prediction transportation system for pedestrian crossing using machine learning

Abstract

A Pedestrian path in road crossing is an essential infrastructure in the part of transportation, which help to serve in the of secure and saving lives of million people and possessions and helps flow in order of the traffic in roads. The main objective of the proposed prediction system is in the process to detect the pedestrian crossing by considering various behaviors of the pedestrians in different types of lanes. We proposed a technique in which the pre-processing of the captured frame and probabilistic branching of the frame are taken care by the Convolutional Neural Networks (CNN). When the pedestrian crossing is detected, the distance is calculated based on the ratio of the area of ROI between them and the frame area. Based on the calculated distance, the brake is controlled automatically to prevent the accidents in obstructed areas. The proposed technique is useful in road safety applications to reduce the unpredictability in determining the behaviour of the pedestrians while crossing the road. This technique mainly comprises of Convolutional Neural Networks (CNN) which can produce higher range of accuracy of 96.7% than in histogram-oriented gradients (HOG) and support vector machine (SVM) which produces accuracy of 78–80%.