An Innovative Prediction Technique to Detect Pedestrian Crossing Using ARELM Technique

  • A. SumiEmail author
  • T. Santha
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 6)


Monitoring Systems of Automobile Industries and Surveillance Systems use operations based on computer vision to identify objects in motion. Most such applications that employ, pattern identification techniques to detect person on road are done through feature mining and classifier development framework. A learned classifier is arranged over the method of recognizing features that are extracted from the video frames. In this paper, classification of pedestrian features is performed and subsequently the presence of pedestrians is predicted. A new classifier, named Asymmetric Least Squared Approximated Rigid Regression Extreme Machine Learning [ARELM] is proposed for the classification and prediction purposes. This classifier combines the strengths of aLs-SVM that deploys the expectile distance as the measurement for boundary values and RELM in handling the multi collinear data. The proposed classifier improves the accuracy in detecting the pedestrians among the navigating things and ensures better prediction, on comparisons with existing classifiers like SVM, BPN used for the same applications.


Pedestrian detection Machine learning ELM ARELM Asymmetric Least Squares 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceDr. G.R. Damodaran College of ScienceCoimbatoreIndia

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