SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Törő O, Bécsi T, Gáspár P. Sensors (Basel) 2021; 21(2): e472.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s21020472

PMID

unavailable

Abstract

[SafetyLit note: The Probability Hypothesis Density (PHD) filter is a multiple-target filter for recursively estimating the number and the state of a set of targets given a set of observations.]

This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant's perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.


Language: en

Keywords

detection probability; multi-target tracking; occlusion; particle filter; PHD filter

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print