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A linear regression method robust to extreme stationary clutter

© 2020 D. A. Bocharov

Institute for Information Transmission Problems RAS, 127051 Moscow, Bolshoy Karetny per., 19, Russia

Received 30 Sep 2019

The study deals with the task of two-dimensional linear regression of noisy data. The data is represented by pairs of coordinates provided by an image-based object detector and can include outliers along with the regular data. As for main outliers model a stationary clutter is considered, i.e. the coordinates of outliers are close to a fixed but undefined point. The paper introduces a linear regression method that is robust to extreme, i.e. the number of outliers exceeds the number of regular data, stationary noise. The proposed method is based on Hough-analysis of input data coordinate histogram and consists of two sequential stages: estimation of angle and shift parameters. Algorithm based on the proposed method is used in the automatic vehicle classifier for wheels trajectory localization. The precision of proposed algorithms and Welsch m-estimator on simulated and real data is investigated. The conducted experiments show that the algorithms based on the proposed method with the same computational complexity have higher precision of wheels trajectory linear model regression in the cases of presence of extreme stationary clutter.

Key words: grobust linear regression, outliers, object detection, automatic vehicle classifier

DOI: 10.31857/S0235009220010059

Cite: Bocharov D. A. Metod lineinoi regressii, ustoichivyi k ekstremalnym statsionarnym pomekham [A linear regression method robust to extreme stationary clutter]. Sensornye sistemy [Sensory systems]. 2020. V. 34(1). P. 44–56 (in Russian). doi: 10.31857/S0235009220010059


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