Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry
Autor: | Ke Zou, Hao Zhu, Yongfu Li, Hamid Esmaeili Najafabadi, Allan De Freitas |
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Rok vydání: | 2020 |
Předmět: |
050210 logistics & transportation
020301 aerospace & aeronautics Computer science Association (object-oriented programming) track association 05 social sciences local track geometry maximum likelihood estimation 02 engineering and technology lcsh:Chemical technology Track (rail transport) Biochemistry Atomic and Molecular Physics and Optics Article Analytical Chemistry Constraint (information theory) Gaussian mixture model 0203 mechanical engineering 0502 economics and business lcsh:TP1-1185 Track geometry Electrical and Electronic Engineering Instrumentation Algorithm sensor bias |
Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 5, p 1412 (2020) Sensors Volume 20 Issue 5 |
ISSN: | 1424-8220 |
Popis: | Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation&ndash maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods. |
Databáze: | OpenAIRE |
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