Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment

Giannopoulos, E.; Streit, Roy L.; Swaszek, Peter F.

doi:10.1109/adfs.1996.581104

Cited by 24 publications

(7 citation statements)

References 2 publications

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“…In the case of multiple targets, additional data association is required to map multiple tracks to respective targets. Various solutions exist to solve this problem like Joint Probabilistic Data Association [22], Probability Hypothesis Density (PHD) filter [23] and Multi Hypothesis Tracking [24]. Within the frameworks presented by these techniques, the computational complexity increases exponentially as the number of targets.…”

Section: Factor Graphs a Overview Of Factor Graphsmentioning

confidence: 99%

Vehicle infrastructure cooperative localization using Factor Graphs

Gulati

Zhang

Clarke

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicleinfrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approach.

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Section: Factor Graphs a Overview Of Factor Graphsmentioning

confidence: 99%

Vehicle infrastructure cooperative localization using Factor Graphs

Gulati

Zhang

Clarke

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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show abstract

“…Various solutions exist for the same like Joint Probabilistic Data Association (JPDA) [10] by Fortmann et al, Probability Hypothesis Density (PHD) filter [11] by Mahler and Multi Hypothesis Tracking [12] by Reid. And there exist various improvements of these solutions like [13], [14] and [15]. But using these additional data association algorithms not only increases the complexity of the task but at times also can increase the execution time.…”

Section: A Overviewmentioning

confidence: 99%

Graph based vehicle infrastructure cooperative localization

Gulati

Zhang

Malovetz

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicleinfrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

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“…After obtaining probability expression for an individual histogram point measurement (shots) falls in a cell, derivation of H-PMHT algorithm is started. H-PMHT stems from PMHT [15], [16], and all derivations of PMHT are based on Expectation Maximization (EM) method. Thus H-PMHT algorithm can be outlined according to expectation (E) and maximization (M) steps.…”

Section: Basic Structure Of H-pmhtmentioning

confidence: 99%

Video Tracking for Visual Degraded Aerial Vehicle with H-PMHT

Pakfiliz¹

2015

RADIOENGINEERING

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The work presented in this paper describes a novel approach for automatic video tracking of visual degraded air vehicles in daylight with sky background. The offered and applied video object tracking method is based on Histogram Probabilistic Multi Hypothesis Tracker algorithm. The H-PMHT is an expectation maximization based algorithm developed for tracking objects in intense clutter environment by using intensity modulated data streams. Basically H-PMHT algorithm is suitable for linear-Gaussian point spread function case. However, recent studies have indicated that the algorithm is also applicable for non-linear and non-Gaussian target shapes. Thus H-PMHT becomes a suitable alternative for tracking applications with sonar, high resolution radars, IR, UV sensors and cameras. In this work H-PMHT algorithm is used for video tracking of visual degraded air vehicles. For this purpose RGB video data is processed by using a reciprocal pixel intensity measurement for meeting the requirements of the tracking process. A simulation study is conducted in order to demonstrate the video tracking performance of H-PMHT against visual degraded air vehicles. Also the results obtained with H-PMHT algorithm are compared with the results of amplitude information added Interacting Multi Model Probabilistic Data Association algorithm.

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Probabilistic multi-hypothesis tracking in a multi-sensor, multi-target environment

Cited by 24 publications

References 2 publications

Vehicle infrastructure cooperative localization using Factor Graphs

Vehicle infrastructure cooperative localization using Factor Graphs

Graph based vehicle infrastructure cooperative localization

Video Tracking for Visual Degraded Aerial Vehicle with H-PMHT

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