Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models

Clark, Dan E.; Vo, Ba-Tuong; Vo, Ba-Ngu; Godsill, Simon

doi:10.1049/ic:20080053

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…In addition, to accommodate nonlinear dynamics and measurement models, several different nonlinear extensions of the GM-PHD are also proposed in the literature [40], [41]. These nonlinear extensions of the GM-PHD filter have successfully been used in many different applications, in which nonlinear target dynamics and measurement models are employed [42]- [48].…”

Section: The Gaussian Mixture Phd (Gm-phd) Filtermentioning

confidence: 99%

Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters

2015

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Abstract-In this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach.

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Section: The Gaussian Mixture Phd (Gm-phd) Filtermentioning

confidence: 99%

Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters

2015

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“…The algorithm has high complexity order due to performing complete data association. Remedies to reduce the number of hypotheses and computational costs have been presented in [Zhao et al, 2005] and [Clark et al, 2008]. JPDAF uses joint probability of measurement-to-object associations in order to find corresponding pairs.…”

Section: Related Workmentioning

confidence: 99%

Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling

Abbaspour,

Masnadi-Shirazi

2020

Preprint

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In this paper, we propose an online multi-object tracking (MOT) method in a delta Generalized Labeled Multi-Bernoulli (δ-GLMB) filter framework to address occlusion and miss-detection issues, reduce false alarms, and recover identity switch (ID switch). To handle occlusion and miss-detection issues, we propose a measurement-to-disappeared track association method based on one-step δ-GLMB filter, so it is possible to manage these difficulties by jointly processing occluded or missdetected objects. This part of proposed method is based on a proposed similarity metric which is responsible for defining the weight of hypothesized reappeared tracks. We also extend the δ-GLMB filter to efficiently recover switched IDs using the cardinality density, size and color features of the hypothesized tracks. We also propose a novel birth model to achieve more effective clutter removal performance. In both occlusion/miss-detection handler and newly-birthed object detector sections of the proposed method, unassigned measurements play a significant role, since they are used as the candidates for reappeared or birth objects. In addition, we perform an ablation study which confirms the effectiveness of our contributions in comparison with the baseline method. We evaluate the proposed method on well-known and publicly available MOT15 and MOT17 test datasets which are focused on pedestrian tracking. Experimental results show that the proposed tracker performs better or at least at the same level of the state-of-the-art online and offline MOT methods. It effectively handles the occlusion and ID switch issues and reduces false alarms as well.

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“…However, these multi-target trackers are not analytical. In [22] and [23], Vo et al propose the particle and Gaussian mixture implementation of these algorithms [24][25][26]. A large number of particles or Gaussian elements are used to approximate the multi-target state distribution.…”

Section: Multi-target Tracking Based On Unlabeled Rfsmentioning

confidence: 99%

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Jing

Leung

2018

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Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.

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Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models

Cited by 13 publications

References 20 publications

Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters

Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters

Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

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