ORCID Profile
0000-0002-4800-6554
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Electrical and Electronic Engineering | Signal Processing | Stochastic Analysis and Modelling | Automotive Safety Engineering
Expanding Knowledge in Engineering | Road Safety | Expanding Knowledge in the Information and Computing Sciences |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: Elsevier BV
Date: 2018
Publisher: MDPI AG
Date: 29-04-2019
DOI: 10.3390/S19092016
Abstract: This paper presents a novel Track-Before-Detect (TBD) Labeled Multi-Bernoulli (LMB) filter tailored for industrial mobile platform safety applications. At the core of the developed solution is two techniques for fusion of color and edge information in visual tracking. We derive an application specific separable likelihood function that captures the geometric shape of the human targets wearing safety vests. We use a novel geometric shape likelihood along with a color likelihood to devise two Bayesian updates steps which fuse shape and color related information. One approach is sequential and the other is based on weighted Kullback–Leibler average (KLA). Experimental results show that the KLA based fusion variant of the proposed algorithm outperforms both the sequential update based variant and a state-of-art method in terms of the performance metrics commonly used in computer vision literature.
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 10-02-2020
DOI: 10.3390/S20030929
Abstract: One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.
Publisher: IEEE
Date: 10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: IEEE
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Elsevier BV
Date: 06-2020
Publisher: IEEE
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2014
Publisher: MDPI AG
Date: 03-04-2019
DOI: 10.3390/S19071614
Abstract: There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2019
No related organisations have been discovered for Amirali Khodadadian Gostar.
Start Date: 07-2021
End Date: 06-2025
Amount: $403,775.00
Funder: Australian Research Council
View Funded Activity