A novel track initialization algorithm based on random sample consensus in dense clutter

Feng Yang,Weikang Tang,Yan Liang

doi:10.1177/1729881418812632

Abstract

Track initialization in dense clutter environments is an important topic and still a challenging task. Most traditional track initialization techniques firstly consider all possible associated measurement combinations and select the optimal one as an initialized track. Therefore, dense clutter brings great challenges to traditional algorithms. Random sample consensus algorithm, which is different from traditional algorithms, starts from minimum measurements. It samples randomly minimum measurements to establish hypotheses and verifies them through remaining measurements. However, the randomness of sampling influences algorithm performance, especially in dense clutter. A novel track initialization based on random sample consensus, named density-based random sample consensus algorithm, is proposed. It utilizes the fact that sequential measurements originating from the same target are contiguous while clutter is separated in space–time domain. The algorithm defines the density property of measurements to decrease the randomness in sampling procedure and increase the efficiency of track initialization. The experimental results show that the density-based random sample consensus is more superior to random sample consensus, the Hough transform algorithm, and logic-based algorithm.

Highlights

A track initialization algorithm based on random sample consensus (RANSAC) and DB-RANSAC is proposed to initialize underlying targets in complicated environment
RANSAC is a traditional algorithm in computer vision and image processing community. It is applied on track initialization recently
DB-RANSAC uses the distribution of measurement in space and time as a prior information to direct sampling, which avoids random sampling and improves the efficiency of initialization

Summary

Introduction

Multiple target tracking (MTT) is an active and hot research area and widely applied in military and civil field.[1,2,3,4,5] Track initialization is the fundamental part of MTT.[6,7,8,9] The long distance between targets and sensors, the inferior detectivity of sensors, and the inaccuracy of received measurement bring great challenges to track initialization, especially in dense clutter.[10,11,12]. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing This method receives observations of some sequential scans and extracts straight lines as target tracks. The hypothesis, which is supported by measurements in every step, is initialized as a target trajectory This novel idea considers a part of combinations instead of all combinations, which especially in dense clutter has a computational advantage than considering all association combinations. The novel algorithm proposed in this article improves standard algorithm to decrease the randomness in sampling procedure and increase the efficiency of track initialization. We assume that the target is moving with constant velocity (CV) and F is the state transition matrix given by

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Conclusion