Adaptive measurement-assignment marginal multi-target Bayes filter with logic-based track initiation

Liu, Zongxiang; Chen, Jinjiang; Zhu, Jiangbo; Li, Liang-qun

doi:10.1016/j.dsp.2022.103636

Cited by 4 publications

(10 citation statements)

References 32 publications

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“…The multi-target Bayes filter uses the sets 𝑋 𝑡 and 𝑍 𝑡 to predict and update the states and observations of multiple targets [3]. The prediction step is represented by: 𝑝(𝑋 𝑡+1 |𝑍 0:𝑡 ) = ∫ 𝑝(𝑋 𝑡+1 |𝑋 𝑡 ) 𝑝(𝑋 𝑡 |𝑍 0:𝑡 ) 𝑑𝑋 𝑡 (5) Where 𝑝(𝑋 𝑡+1 |𝑋 𝑡 ) the transition is model and 𝑝(𝑋 𝑡+1 |𝑍 0:𝑡 ) is the posterior density from the previous time step.…”

Section: Bayes Filtermentioning

confidence: 99%

“…Multi-target tracking is an essential task in computer vision and has important applications in various fields [1], involving the tracking of objects with varying states over time in sequences of video frames [2,3]. However, this problem faces several challenges that make it difficult to solve, including image occlusion, image clutter, and low image quality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Mehrizi,

Yazdi

2024

Preprint

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This paper presents an innovative approach to vehicle tracking in video streams, utilizing graph structures, integrated prior knowledge, and forecasting techniques. The method encompasses statistical analysis and prediction of non-identical periodic events, specifically tailored to the characteristics of vehicles in motion. Through the generation of pseudo-observations, auxiliary information is provided to the tracker, effectively capturing the underlying dynamics of vehicle movements. The approach is evaluated on tracking datasets, demonstrating substantial improvements in tracking performance, especially in scenarios with camera sensor occlusion caused by physical obstructions, which is a common challenge encountered in camera-based tracking systems.

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Section: Bayes Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Mehrizi,

Yazdi

2024

Preprint

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“…The main weakness of the AGLMB filter is that some of the newborn tracks may be false tracks. To decrease the false-track probability, combining the logic-based track initiation technique into the marginal multi-target Bayesian (MTB) filter, Liu et al proposed the adaptive MTB filter with assignment of measurements (AAMMTB filter) [35]. Compared with the AGLMB filter [34], the AAMMTB filter achieves high tracking accuracy at a low computational cost [35].…”

Section: Introductionmentioning

confidence: 99%

“…To decrease the false-track probability, combining the logic-based track initiation technique into the marginal multi-target Bayesian (MTB) filter, Liu et al proposed the adaptive MTB filter with assignment of measurements (AAMMTB filter) [35]. Compared with the AGLMB filter [34], the AAMMTB filter achieves high tracking accuracy at a low computational cost [35]. The AGLMB filter and AAMMTB filter use the observations of three time steps to generate the birth tracks.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Hypothesis Marginal Bayes Filter for Tracking Multiple Targets

Liu,

Qiu,

Gao

et al. 2024

Remote Sensing

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Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters.

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“…Multi-target tracking (MTT) is the process of estimating the states of multiple moving targets at different time steps according to a set of sensor observations. It has received extensive attention from scholars [1][2][3][4][5][6][7][8] due to its wide application in many real systems, such as intelligent transportation systems, video surveillance systems, radar tracking systems, etc. Two major groups of MTT algorithms have been reported in a lot of articles [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise

Liu,

Luo,

Zhou

2023

Remote Sensing

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A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student’s t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters.

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Adaptive measurement-assignment marginal multi-target Bayes filter with logic-based track initiation

Cited by 4 publications

References 32 publications

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Adaptive Multi-Hypothesis Marginal Bayes Filter for Tracking Multiple Targets

Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise

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