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Human Behaviour Analysis Using Intelligent Systems pp 119–140Cite as

An Innovative Prediction Technique to Detect Pedestrian Crossing Using ARELM Technique

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Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 6))

Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, Dr. G.R. Damodaran College of Science, Coimbatore, Tamil Nadu, 641014, India

    A. Sumi & T. Santha

Authors
  1. A. Sumi
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  2. T. Santha
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Correspondence to A. Sumi .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

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Sumi, A., Santha, T. (2020). An Innovative Prediction Technique to Detect Pedestrian Crossing Using ARELM Technique. In: Hemanth, D. (eds) Human Behaviour Analysis Using Intelligent Systems. Learning and Analytics in Intelligent Systems, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-35139-7_6

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