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

Jing, Zhongliang; Li, Minzhe; Leung, Henry

doi:10.1007/s42401-018-0003-2

Cited by 5 publications

(2 citation statements)

References 57 publications

(62 reference statements)

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“…[9] used a novel framework for the road estimation task through the incorporation of reliability into the multi-source fusion and the integration of an offlinetrained knowledge base for the reliability assessment represented by Bayesian Network or Random Forests. Jing et al [10] used a new algorithm for multisensor multi-target joint detection, tracking, and classification problems. A constitutional multi-sensor multi-target state estimator was derived, and the optimal solution was obtained based on the minimum Bayes risk criterion.…”

Section: Related Workmentioning

confidence: 99%

Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

2020

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In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.

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Section: Related Workmentioning

confidence: 99%

Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

2020

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“…Multiple target joint detection, tracking and classification is a critical problem in radar system, this problem consists of three subproblems: estimate the number of the targets, estimate their kinematic states and determine their classes. These three subproblems are usually coupled: tracking provides the kinematic features to distinguish the target type; according to the target class, appropriate dynamic models can be chosen for accurate tracking; besides, the detection of the target is the prerequisite of accurate multi-target tracking and classification [ 1 , 2 , 3 ]. Therefore, multi-target detection, tracking and classification need to be solved jointly.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Liang

Jing

et al. 2020

Sensors

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This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.

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Multi-target joint tracking and classification based on joint decision and estimation

Zhang,

2024

SIViP

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

Cited by 5 publications

References 57 publications

Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Multi-target joint tracking and classification based on joint decision and estimation

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