Multiple Target Tracking for Mobile Robots Using the JPDAF Algorithm

Gorji, Aliakbar A.; Menhaj, Mohammad Bagher; Shiry, Saeed

doi:10.1007/978-3-540-88069-1_5

Cited by 5 publications

(8 citation statements)

References 14 publications

(14 reference statements)

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“…To have the same theoretical basis, a comparison to a popular target tracking method [9] based on Probabilistic Data Association and a particle filtering (PDA-PF) is proposed.…”

Section: A Simulated Datamentioning

confidence: 99%

“…Detection methods need robust segmentation of telemetric data and eventually they model objects by building bounding-boxes from extracted features [4], [5], [6]. Usually, tracking methods are based on filtering techniques depending on a state-space modeling and involving an association procedure, like probabilistic data association (PDA) or multi-hypothesis tracking (MHT) [7], [8], [9]. Unfortunately, imperfections in detection stage, due to non detection and false alarms, lead to association errors and disturb the tracking procedure.…”

Section: Introductionmentioning

confidence: 99%

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A particle filtering approach for joint vehicular detection and tracking in lidar data

Fortin¹,

Noyer²,

Lherbier³

2012

2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings

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This paper presents a method for joint detection and tracking of vehicles in scanning laser range data. Many methods use a solution that processes the raw data in a detection procedure and then tracks the detected object in an association/tracking procedure. The proposed approach uses a preclustering stage (SIP) as an input of the tracking process that allows to manage the displacement of the center-of-gravity and the changes in the apparent shape from object and motion modeling. The global problem is then described using a state-space modeling which is solved by a nonlinear filtering method.

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“…To have the same theoretical basis, a comparison to a popular target tracking method [9] based on Probabilistic Data Association and a particle filtering (PDA-PF) is proposed.…”

Section: A Simulated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A particle filtering approach for joint vehicular detection and tracking in lidar data

Fortin¹,

Noyer²,

Lherbier³

2012

2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings

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“…Specifically, targets ρ = 1, 2 start moving at positions [1.5, 11.5], [ 5,7] and follow the dynamics in (1) , where different sets of targets are moving on the field. It should be emphasized that it is not known that at t = 30 and t = 50, the target configuration is changing.…”

Section: Simulationsmentioning

confidence: 99%

“…Existing works [5][6][7][8] associate measurements acquired at static sensors with targets across time and rely heavily on probability models. A distributed Kalman filtering scheme is proposed in [9] relying on information diffusion strategies.…”

Section: Introductionmentioning

confidence: 99%

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Ren

Maroulas

Schizas

2016

EURASIP J. Adv. Signal Process.

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The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

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“…The particle filter-based data association methods [7,8], modified from JPDAF based on the Kalman filter, are robust in tracking multiple targets with nonlinear property. However, these techniques are originally developed for radar data, and they require modification to apply to the visual tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Disjoint Particle Filter to Track Multiple Objects in Real-time

2014

KSII TIIS

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Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

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Multiple Target Tracking for Mobile Robots Using the JPDAF Algorithm

Cited by 5 publications

References 14 publications

A particle filtering approach for joint vehicular detection and tracking in lidar data

A particle filtering approach for joint vehicular detection and tracking in lidar data

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Disjoint Particle Filter to Track Multiple Objects in Real-time

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