Open AccessDirection‐Based Modified Particle Filter for Vehicle Tracking
Author(s) -
Yildirim Mustafa Eren,
Ince Ibrahim Furkan,
Salman Yucel Batu,
Song Jong Kwan,
Park Jang Sik,
Yoon Byung Woo
Publication year - 2016
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.16.0115.0181
Subject(s) - tracking (education) , particle filter , particle (ecology) , filter (signal processing) , control theory (sociology) , auxiliary particle filter , computer science , artificial intelligence , computer vision , kalman filter , ensemble kalman filter , extended kalman filter , psychology , pedagogy , oceanography , geology , control (management)
This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.