A joint data association, registration, and fusion approach for distributed tracking

Zhu, Hao; Leung, Henry; Yuen, Ka‐Veng

doi:10.1016/j.ins.2015.06.042

Cited by 38 publications

(18 citation statements)

References 33 publications

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“…In [142], based on equivalent measurements, a joint sensor registration and trackto-track fusion approach was proposed. In [143], using a pseudo-measurement approach, a joint registration, association and fusion method at distributed architecture was developed. In [144], using information matrix fusion, a track-to-track fusion approach was presented for automotive environment perception.…”

Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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307

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

307

125

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“…Different from the centralized joint registration and fusion [80], which carried out at the measurement level for multisensor fusion, the truly distributed joint registration and fusion in sensor network was addressed in [88], where an EM algorithm is developed to perform the track registration, data association and fusion simultaneously at the track level. Moreover, Lan et al [28] proposed and compared two centralized EM algorithms and three consensus-based distributed EM algorithms for joint state estimation and identification of sensor networks with unknown inputs.…”

Section: Distributed Target Tracking In Sensor Networkmentioning

confidence: 99%

A survey on joint tracking using expectation–maximization based techniques

et al. 2016

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Many target tracking problems can actually be cast as joint tracking problems where the underlying target state may only be observed via the relationship with a latent variable. In the presence of uncertainties in both observations and latent variable, which encapsulates the target tracking into a variational problem, the expectation-maximization (EM) method provides an iterative procedure under Bayesian inference framework to estimate the state of target in the process which minimises the latent variable uncertainty. In this paper, we treat the joint tracking problem using a united framework under the EM method and provide a comprehensive overview of various EM approaches in joint tracking context from their necessity, benefits, and challenging viewpoints. Some examples on the EM application idea are presented. In addition, future research directions and open issues for using EM method in the joint tracking are given.

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“…Literature addressing this problem can be roughly divided into batch and online approaches. The batch approach is an offline implementation that estimates the track association and sensor bias using all local tracks [30][31][32]. A joint sensor registration and track-to-track fusion method is derived using an equivalent measurement method in [30], while a pseudo-measurement approach is adopted to handle registration and track fusion simultaneously in [31].…”

Section: Introductionmentioning

confidence: 99%

“…A joint sensor registration and track-to-track fusion method is derived using an equivalent measurement method in [30], while a pseudo-measurement approach is adopted to handle registration and track fusion simultaneously in [31]. In [32], a joint registration, data association, and fusion method in a distributed sensor network is formulated as a maximum likelihood (ML) optimization problem. An expectation-maximization (EM) algorithm is then proposed to perform the ML optimization, joint association, and bias removal through following an iterative strategy.…”

Section: Introductionmentioning

confidence: 99%

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Zhu

Freitas

et al. 2020

Sensors

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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-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.system in intelligent vehicles [7][8][9]. A multi-sensor multi-target tracking module jointly estimates the states and the number of targets from sensor measurements in intelligent vehicles, and it can be broadly categorized as centralized or distributed. The advantage of the distributed tracking systems is that they can provide a degree of scalability and robustness not achievable by traditional centralized tracking systems [1].Track-to-track association (T2TA) is a crucial task in distributed tracking to find the correspondence between local tracks from different sensors. It is commonly applied to combine the local tracks of a sensor with those of another sensor to form the global tracklist. For automotive applications, radar, lidar, and video sensors in environmental perception systems for intelligent vehicles use different coordinate systems and sampling frequencies. Therefore, a spatio-temporal calibration should be performed to align the detections from different sensors [1]. In practice, detection from radar, lidar, and video sensors cannot always be calibrated or aligned accurately [10]. Each sensor may cover a different part of the surveillance region with a detection probability of less than one. As a result, some local tracks from a sensor may not correspond to those of other sensors. The range, azimuth, and elevation biases of a radar sensor may lead to errors in the local tracks from that sensor. The relationship between radar sensor bias and local tracks is presented in Figure 1, where two radar sensors, A and B, and one target, T, are shown. The radar sensor bias leads to the reporting of the target T as tracks T A and...

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A joint data association, registration, and fusion approach for distributed tracking

Cited by 38 publications

References 33 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

A survey on joint tracking using expectation–maximization based techniques

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

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