Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841303
Runze Gan, Qing Li, S. Godsill
The non-homogeneous Poisson process (NHPP) has been widely used to model extended object measurements where one object can generate zero or several measurements; it also provides an elegant solution to the computationally demanding data association problem in multiple object tracking. This paper presents an association-based NHPP system, based on which we propose a variational Bayes association-based NHPP (VB-AbNHPP) tracker that can estimate online the object kinematics and the association variables in parallel. In particular, the VB-AbNHPP tracker can be easily extended to include online static parameter learning (e.g., measurement rates) based on a general coordinate ascent variational filtering framework developed here. The results show that the proposed VB-AbNHPP tracker is superior to other competing methods in terms of implementation efficiency and tracking accuracy.
{"title":"A Variational Bayes Association-based Multi-object Tracker under the Non-homogeneous Poisson Measurement Process","authors":"Runze Gan, Qing Li, S. Godsill","doi":"10.23919/fusion49751.2022.9841303","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841303","url":null,"abstract":"The non-homogeneous Poisson process (NHPP) has been widely used to model extended object measurements where one object can generate zero or several measurements; it also provides an elegant solution to the computationally demanding data association problem in multiple object tracking. This paper presents an association-based NHPP system, based on which we propose a variational Bayes association-based NHPP (VB-AbNHPP) tracker that can estimate online the object kinematics and the association variables in parallel. In particular, the VB-AbNHPP tracker can be easily extended to include online static parameter learning (e.g., measurement rates) based on a general coordinate ascent variational filtering framework developed here. The results show that the proposed VB-AbNHPP tracker is superior to other competing methods in terms of implementation efficiency and tracking accuracy.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"64 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841359
Xihong Zhong, L. Li
To achieve object detection on low-computing devices such as embedded and mobile devices, we propose a new method of the non-deep network for fast vehicle detection. In our method, we use color transformation to address the problem of insufficient training data. To achieve effective object detection of different sizes, we introduce a non-deep network with a parallel double-stream design. The upper stream adopts the downsampling block which contains a 3*3 kernel size convolution layer to extract the feature of small objects. The lower stream uses the downsampling block which contains a 5*5 kernel size convolution layer to realize the detection of large targets. Finally, these extracted features of different receptive fields are fused in the fusion block. We use the one-level output feature from backbone for detection to improve model efficiency. The experimental results show that our network runs real-time on Jetson TX2, and achieves 30.46% mAP on COCO and 77.2% mAP on UA-DETRAC. Our detector is more accurate than YOLO-Fastest and faster than YOLOv4-tiny.
{"title":"A New Method of Non-Deep Network for Fast Vehicle Detection","authors":"Xihong Zhong, L. Li","doi":"10.23919/fusion49751.2022.9841359","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841359","url":null,"abstract":"To achieve object detection on low-computing devices such as embedded and mobile devices, we propose a new method of the non-deep network for fast vehicle detection. In our method, we use color transformation to address the problem of insufficient training data. To achieve effective object detection of different sizes, we introduce a non-deep network with a parallel double-stream design. The upper stream adopts the downsampling block which contains a 3*3 kernel size convolution layer to extract the feature of small objects. The lower stream uses the downsampling block which contains a 5*5 kernel size convolution layer to realize the detection of large targets. Finally, these extracted features of different receptive fields are fused in the fusion block. We use the one-level output feature from backbone for detection to improve model efficiency. The experimental results show that our network runs real-time on Jetson TX2, and achieves 30.46% mAP on COCO and 77.2% mAP on UA-DETRAC. Our detector is more accurate than YOLO-Fastest and faster than YOLOv4-tiny.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841394
Qi Tang, Z. Duan, Donglin Zhang, X. Li
Cross-correlation generally exists between local estimation errors in distributed estimation fusion but is hard to know exactly. In some cases, the cross-correlation is partially known (e.g., only correlation level is known approximately or known within an interval). Utilizing correlation information benefits estimation. To use it, one way is to model the correlation by a generalized Pearson's correlation coefficient times the matrix product of local MSE matrices. The correlation coefficient is used to measure linear relation between two random vectors, and is assumed to be random (e.g., uniformly distributed) over the provided interval. Based on the way the cross-covariance matrix is modeled, the assumption about correlation coefficient and by applying best linear unbiased estimation (BLUE) fusion, an estimation fusion algorithm, called expected BLUE fuser (EBF), is presented. Compared with similar algorithms, its comparable or better performance demonstrates its effectiveness. Considering the fusion results under various given correlation intervals, we observe that strong correlation benefits fusion performance, and point out that EBF gets good fusion results when the given correlation interval cover the true correlation coefficient tightly.
{"title":"Estimation Fusion Based on Simplified Model for Cross-Covariance of Local Estimation Errors","authors":"Qi Tang, Z. Duan, Donglin Zhang, X. Li","doi":"10.23919/fusion49751.2022.9841394","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841394","url":null,"abstract":"Cross-correlation generally exists between local estimation errors in distributed estimation fusion but is hard to know exactly. In some cases, the cross-correlation is partially known (e.g., only correlation level is known approximately or known within an interval). Utilizing correlation information benefits estimation. To use it, one way is to model the correlation by a generalized Pearson's correlation coefficient times the matrix product of local MSE matrices. The correlation coefficient is used to measure linear relation between two random vectors, and is assumed to be random (e.g., uniformly distributed) over the provided interval. Based on the way the cross-covariance matrix is modeled, the assumption about correlation coefficient and by applying best linear unbiased estimation (BLUE) fusion, an estimation fusion algorithm, called expected BLUE fuser (EBF), is presented. Compared with similar algorithms, its comparable or better performance demonstrates its effectiveness. Considering the fusion results under various given correlation intervals, we observe that strong correlation benefits fusion performance, and point out that EBF gets good fusion results when the given correlation interval cover the true correlation coefficient tightly.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116543152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841264
Li-wen Guo, Sanfeng Hu, Jie Zhou, X. Li
In the Bayesian filtering paradigm, approximation of posterior distribution is an important research topic for nonlinear dynamic systems. In this paper, we aim at obtaining its Gaussian approximation via KL divergence minimization. We formulate the problem as a nonlinear programming problem with linear constraints and resort to the feasible direction method for the solution. Since the gradient of the objective function involves intractable integrals, we adopt a cubature rule to calculate the gradient, which is suitable for real-time filtering for its simplicity, efficiency, and accuracy. Based on the Gaussian approximation, a nonlinear filter is derived, and it is demonstrated to be effective by simulations.
{"title":"Gaussian Approximation Filter Based on Divergence Minimization for Nonlinear Dynamic Systems","authors":"Li-wen Guo, Sanfeng Hu, Jie Zhou, X. Li","doi":"10.23919/fusion49751.2022.9841264","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841264","url":null,"abstract":"In the Bayesian filtering paradigm, approximation of posterior distribution is an important research topic for nonlinear dynamic systems. In this paper, we aim at obtaining its Gaussian approximation via KL divergence minimization. We formulate the problem as a nonlinear programming problem with linear constraints and resort to the feasible direction method for the solution. Since the gradient of the objective function involves intractable integrals, we adopt a cubature rule to calculate the gradient, which is suitable for real-time filtering for its simplicity, efficiency, and accuracy. Based on the Gaussian approximation, a nonlinear filter is derived, and it is demonstrated to be effective by simulations.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125156722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841296
Jiří Ajgl, O. Straka
The process of combining data and estimates is inherent in estimation problems. This paper focuses on the linear fusion under the assumption that only some elements of the cross-correlation matrix of the estimation errors are known. Configurations of the knowledge are discussed individually for up to five estimates. For an arbitrary number of estimates, a general construction of upper bounds of the joint mean square error matrix is proposed. Last, the relation with the Split Covariance Intersection fusion is discussed.
{"title":"Linear Fusion with Element-Wise Knowledge","authors":"Jiří Ajgl, O. Straka","doi":"10.23919/fusion49751.2022.9841296","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841296","url":null,"abstract":"The process of combining data and estimates is inherent in estimation problems. This paper focuses on the linear fusion under the assumption that only some elements of the cross-correlation matrix of the estimation errors are known. Configurations of the knowledge are discussed individually for up to five estimates. For an arbitrary number of estimates, a general construction of upper bounds of the joint mean square error matrix is proposed. Last, the relation with the Split Covariance Intersection fusion is discussed.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper mainly addresses the scalable detection and tracking of the extended target in the low signal-to-noise(SNR) environment. As the appearance and shape of the extended target are constantly varied, it is challenging to achieve robust detection and tracking. For this, a novel adaptive scale (AS) kernelized correlation filter (KCF) based on multi-frame track-before-detect (MF-TBD) framework is proposed. By embedding scaling pools into the response map to handle the scale variation and accumulating target energy overall feasible trajectories, AS-MF-TBD estimates the kinematic state and geometric shapes simultaneously. Both simulation data and real radar data are used to demonstrate the superiority of the proposed method in terms of detection performance and estimation accuracy.
{"title":"Multi-Frame Track-Before-Detect for Scalable Extended Target Tracking","authors":"Desheng Zhang, Wujun Li, Shixing Yang, Yingshun Wang, Chuan Zhu, Wei Yi","doi":"10.23919/fusion49751.2022.9841326","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841326","url":null,"abstract":"This paper mainly addresses the scalable detection and tracking of the extended target in the low signal-to-noise(SNR) environment. As the appearance and shape of the extended target are constantly varied, it is challenging to achieve robust detection and tracking. For this, a novel adaptive scale (AS) kernelized correlation filter (KCF) based on multi-frame track-before-detect (MF-TBD) framework is proposed. By embedding scaling pools into the response map to handle the scale variation and accumulating target energy overall feasible trajectories, AS-MF-TBD estimates the kinematic state and geometric shapes simultaneously. Both simulation data and real radar data are used to demonstrate the superiority of the proposed method in terms of detection performance and estimation accuracy.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841270
A. D'Ortenzio, C. Manes, U. Orguner
In order to be properly addressed, many practical problems require an accurate stochastic characterization of the involved uncertainties. In this regard, a common approach is the use of mixtures of parametric densities which allow, in general, to arbitrarily approximate complex distributions by a sum of simpler elements. Nonetheless, in contexts like target tracking in clutter, where mixtures of densities are commonly used to approximate the posterior distribution, the optimal Bayesian recursion leads to a combinatorial explosion in the number of mixture components. For this reason, many mixture reduction algorithms have been proposed in the literature to keep limited the number of hypotheses, but very few of them have addressed the problem of finding a suitable model order for the resulting approximation. The commonly followed approach in those algorithms is to reduce the mixture to a fixed number of components, disregarding its features which may vary over time. In general, finding an optimal number of mixture components is a very difficult task: once a meaningful optimality criterion is identified, potentially burdensome computational procedures must be devised to reach the optimum. In this work, by exploiting the optimal transport theory, an efficient and intuitive model selection criterion for the mixture reduction problem is proposed.
{"title":"A Model Selection criterion for the Mixture Reduction problem based on the Kullback - Leibler Divergence","authors":"A. D'Ortenzio, C. Manes, U. Orguner","doi":"10.23919/fusion49751.2022.9841270","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841270","url":null,"abstract":"In order to be properly addressed, many practical problems require an accurate stochastic characterization of the involved uncertainties. In this regard, a common approach is the use of mixtures of parametric densities which allow, in general, to arbitrarily approximate complex distributions by a sum of simpler elements. Nonetheless, in contexts like target tracking in clutter, where mixtures of densities are commonly used to approximate the posterior distribution, the optimal Bayesian recursion leads to a combinatorial explosion in the number of mixture components. For this reason, many mixture reduction algorithms have been proposed in the literature to keep limited the number of hypotheses, but very few of them have addressed the problem of finding a suitable model order for the resulting approximation. The commonly followed approach in those algorithms is to reduce the mixture to a fixed number of components, disregarding its features which may vary over time. In general, finding an optimal number of mixture components is a very difficult task: once a meaningful optimality criterion is identified, potentially burdensome computational procedures must be devised to reach the optimum. In this work, by exploiting the optimal transport theory, an efficient and intuitive model selection criterion for the mixture reduction problem is proposed.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841313
J. Z. Hare, L. Kaplan
In this work, we study the problem of Quickest Change Detection which aims to detect when a stream of observations transitions from being drawn from a pre-change distribution to a post-change distribution as quickly as possible. Traditionally, either information is completely known about the distributions, or no information is known and their parameters are estimated using frequentist approaches, e.g., Generalized Likelihood Ratio test. Recently, the Uncertain Likelihood Ratio (ULR) test was proposed for the QCD problem which relaxes both of these assumptions to form a Bayesian test that allows for no knowledge, partial knowledge, and full knowledge of the parameters of the distributions. In this work, we extend the ULR test to improve the order of operations required to compute the test statistic using a windowing method to form the Windowed Uncertain Likelihood Ratio (W-ULR) algorithm. We then applied it to multivariate Gaussian observations and empirically evaluated the average detection delay and missed detections for various false alarm rates under various operating conditions. The results show that the W-ULR outperforms the (windowed) GLR test, which is consistent with the initial findings.
{"title":"Uncertainty-Aware Quickest Change Detection: An Experimental Study","authors":"J. Z. Hare, L. Kaplan","doi":"10.23919/fusion49751.2022.9841313","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841313","url":null,"abstract":"In this work, we study the problem of Quickest Change Detection which aims to detect when a stream of observations transitions from being drawn from a pre-change distribution to a post-change distribution as quickly as possible. Traditionally, either information is completely known about the distributions, or no information is known and their parameters are estimated using frequentist approaches, e.g., Generalized Likelihood Ratio test. Recently, the Uncertain Likelihood Ratio (ULR) test was proposed for the QCD problem which relaxes both of these assumptions to form a Bayesian test that allows for no knowledge, partial knowledge, and full knowledge of the parameters of the distributions. In this work, we extend the ULR test to improve the order of operations required to compute the test statistic using a windowing method to form the Windowed Uncertain Likelihood Ratio (W-ULR) algorithm. We then applied it to multivariate Gaussian observations and empirically evaluated the average detection delay and missed detections for various false alarm rates under various operating conditions. The results show that the W-ULR outperforms the (windowed) GLR test, which is consistent with the initial findings.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122803220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841307
Christian Kinzig, Irene Cortés, C. Fernández, M. Lauer
Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.
{"title":"Real-time Seamless Image Stitching in Autonomous Driving","authors":"Christian Kinzig, Irene Cortés, C. Fernández, M. Lauer","doi":"10.23919/fusion49751.2022.9841307","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841307","url":null,"abstract":"Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124044653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841344
Timothy J. Glover, Cunjia Liu, Wen-Hua Chen
Incorporating prior environmental information to the diversely applied field of target tracking is becoming more beneficial in order to leverage maximum sensing performance. Urban scenarios in particular present numerous large obstacles that will block the field of view of most sensors along with many potential sources of measurement clutter. Handling these complications appropriately is imperative to ensure improved target tracking performance. This paper presents a computationally efficient method of integrating visibility information within the Bernoulli particle filter. Through estimation of target visibility with ray casting, the probability of target detection, birth density and spatial target density are modified. Numerical results demonstrate significantly more gradual degradation in target state estimation performance and improved estimation of target existence in the occlusion situation. Faster tracking recovery when emerging from occluded regions is also demonstrated.
{"title":"Visibility Informed Bernoulli Filter for Target Tracking in Cluttered Environments","authors":"Timothy J. Glover, Cunjia Liu, Wen-Hua Chen","doi":"10.23919/fusion49751.2022.9841344","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841344","url":null,"abstract":"Incorporating prior environmental information to the diversely applied field of target tracking is becoming more beneficial in order to leverage maximum sensing performance. Urban scenarios in particular present numerous large obstacles that will block the field of view of most sensors along with many potential sources of measurement clutter. Handling these complications appropriately is imperative to ensure improved target tracking performance. This paper presents a computationally efficient method of integrating visibility information within the Bernoulli particle filter. Through estimation of target visibility with ray casting, the probability of target detection, birth density and spatial target density are modified. Numerical results demonstrate significantly more gradual degradation in target state estimation performance and improved estimation of target existence in the occlusion situation. Faster tracking recovery when emerging from occluded regions is also demonstrated.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132462180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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