Pub Date : 2022-07-04DOI: 10.48550/arXiv.2208.08870
Ariane Hanebeck, C. Czado
This paper proposes a novel method for testing observability in Gaussian models using discrete density approximations (deterministic samples) of (multivariate) Gaussians. Our notion of observability is defined by the existence of the maximum a posteriori estimator. In the first step of the proposed algorithm, the discrete density approximations are used to generate a single representative design observation vector to test for observability. In the second step, a number of carefully chosen design observation vectors are used to obtain information on the properties of the estimator. By using measures like the variance and the so-called local variance, we do not only obtain a binary answer to the question of observability but also provide a quantitative measure.
{"title":"On the Observability of Gaussian Models using Discrete Density Approximations","authors":"Ariane Hanebeck, C. Czado","doi":"10.48550/arXiv.2208.08870","DOIUrl":"https://doi.org/10.48550/arXiv.2208.08870","url":null,"abstract":"This paper proposes a novel method for testing observability in Gaussian models using discrete density approximations (deterministic samples) of (multivariate) Gaussians. Our notion of observability is defined by the existence of the maximum a posteriori estimator. In the first step of the proposed algorithm, the discrete density approximations are used to generate a single representative design observation vector to test for observability. In the second step, a number of carefully chosen design observation vectors are used to obtain information on the properties of the estimator. By using measures like the variance and the so-called local variance, we do not only obtain a binary answer to the question of observability but also provide a quantitative measure.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"129 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":"115231940","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/fusion49751.2022.9841302
K. Berntorp, Marcus Greiff, S. D. Cairano
In this paper we develop a method for vehicle positioning based on global navigation satellite system (GNSS) and camera information. Both GNSS and camera measurements have noise characteristics that vary in time. As a result, the measurements can abruptly change from reliable to unreliable from one time step to another. To adapt to the changing noise levels and hence improve positioning performance, we combine GNSS information with measurements from a forward looking camera, a steering-wheel angle sensor, wheel-speed sensors, and optionally an inertial sensor. We pose the estimation problem in an interacting multiple-model (IMM) setting and use Bayes recursion to choose the best combination of the estimators. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions.
{"title":"Bayesian Sensor Fusion of GNSS and Camera With Outlier Adaptation for Vehicle Positioning","authors":"K. Berntorp, Marcus Greiff, S. D. Cairano","doi":"10.23919/fusion49751.2022.9841302","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841302","url":null,"abstract":"In this paper we develop a method for vehicle positioning based on global navigation satellite system (GNSS) and camera information. Both GNSS and camera measurements have noise characteristics that vary in time. As a result, the measurements can abruptly change from reliable to unreliable from one time step to another. To adapt to the changing noise levels and hence improve positioning performance, we combine GNSS information with measurements from a forward looking camera, a steering-wheel angle sensor, wheel-speed sensors, and optionally an inertial sensor. We pose the estimation problem in an interacting multiple-model (IMM) setting and use Bayes recursion to choose the best combination of the estimators. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"543 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":"123066755","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/fusion49751.2022.9841384
F. Pfaff, Kailai Li, U. Hanebeck
Estimating the position and orientation of 3-D objects is a ubiquitous challenge. In our novel filter, the position and orientation of objects are modeled using the Cartesian product of ℝ for the position and a 3-D hyperhemisphere. The latter is used to describe orientations in the form of unit quaternions. The hyperhemisphere is subdivided into equally sized areas. The joint density for the position and orientation is split up into a marginal density for the orientation and a density for the position that is conditioned on the orientation. In our filter, we assume that the function values of the marginal density and the conditional density is the same for all points within that area. By assuming all conditional densities to be Gaussians, efficient formulae can be implemented for the update and prediction steps. The filter is evaluated based on a simulation scenario, for which it showed very high accuracy at low run times.
{"title":"The State Space Subdivision Filter for SE(3)","authors":"F. Pfaff, Kailai Li, U. Hanebeck","doi":"10.23919/fusion49751.2022.9841384","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841384","url":null,"abstract":"Estimating the position and orientation of 3-D objects is a ubiquitous challenge. In our novel filter, the position and orientation of objects are modeled using the Cartesian product of ℝ for the position and a 3-D hyperhemisphere. The latter is used to describe orientations in the form of unit quaternions. The hyperhemisphere is subdivided into equally sized areas. The joint density for the position and orientation is split up into a marginal density for the orientation and a density for the position that is conditioned on the orientation. In our filter, we assume that the function values of the marginal density and the conditional density is the same for all points within that area. By assuming all conditional densities to be Gaussians, efficient formulae can be implemented for the update and prediction steps. The filter is evaluated based on a simulation scenario, for which it showed very high accuracy at low run times.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"98 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":"115771232","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/fusion49751.2022.9841354
Karthik Comandur, Yunpeng Li, S. Nannuru
State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in high-dimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples.
{"title":"Particle Flow Gaussian Particle Filter","authors":"Karthik Comandur, Yunpeng Li, S. Nannuru","doi":"10.23919/fusion49751.2022.9841354","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841354","url":null,"abstract":"State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in high-dimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"62 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120835718","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/fusion49751.2022.9841358
Giuseppina Carannante, Dimah Dera, Orune Aminul, N. Bouaynaya, G. Rasool
Deep Learning (DL) models have achieved or even surpassed human-level accuracy in several areas, including computer vision and pattern recognition. The state-of-art performance of DL models has raised the interest in using them in real-world applications, such as disease diagnosis and clinical decision support systems. However, the challenge remains the lack of trustworthiness and reliability of these DL models. The detection of incorrect decisions or flagging suspicious input samples is essential for the reliability of machine learning models. Uncertainty estimation in the output decision is a key component in establishing the trustworthiness and reliability of these models. In this work, we use Bayesian techniques to estimate the uncertainty in the model's output and use this uncertainty to detect distributional shifts linked to both input perturbations and labels shifts. We use the learned uncertainty information (i.e., the variance of the predictive distribution) in two different ways to detect anomalous input samples: 1) a static threshold based on average uncertainty of a model evaluated on the clean test data, and 2) a statistical threshold based on the significant increase in the average uncertainty of the model evaluated on corrupted (anomalous) samples. Our extensive experiments demonstrate that both approaches can detect anomalous samples. We observe that the proposed thresholding techniques can distinguish misclassified examples in the presence of noise, adversarial attacks, anomalies or distributional shifts. For example, when considering corrupted versions of MNIST and CIFAR-10 datasets, the rate of detecting misclassified samples is almost twice as compared to Monte-Carlo-based approaches.
{"title":"Self-Assessment and Robust Anomaly Detection with Bayesian Deep Learning","authors":"Giuseppina Carannante, Dimah Dera, Orune Aminul, N. Bouaynaya, G. Rasool","doi":"10.23919/fusion49751.2022.9841358","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841358","url":null,"abstract":"Deep Learning (DL) models have achieved or even surpassed human-level accuracy in several areas, including computer vision and pattern recognition. The state-of-art performance of DL models has raised the interest in using them in real-world applications, such as disease diagnosis and clinical decision support systems. However, the challenge remains the lack of trustworthiness and reliability of these DL models. The detection of incorrect decisions or flagging suspicious input samples is essential for the reliability of machine learning models. Uncertainty estimation in the output decision is a key component in establishing the trustworthiness and reliability of these models. In this work, we use Bayesian techniques to estimate the uncertainty in the model's output and use this uncertainty to detect distributional shifts linked to both input perturbations and labels shifts. We use the learned uncertainty information (i.e., the variance of the predictive distribution) in two different ways to detect anomalous input samples: 1) a static threshold based on average uncertainty of a model evaluated on the clean test data, and 2) a statistical threshold based on the significant increase in the average uncertainty of the model evaluated on corrupted (anomalous) samples. Our extensive experiments demonstrate that both approaches can detect anomalous samples. We observe that the proposed thresholding techniques can distinguish misclassified examples in the presence of noise, adversarial attacks, anomalies or distributional shifts. For example, when considering corrupted versions of MNIST and CIFAR-10 datasets, the rate of detecting misclassified samples is almost twice as compared to Monte-Carlo-based approaches.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"27 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":"125018210","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/fusion49751.2022.9841256
B. Debaque, Hughes Perreault, Jean-Philippe Mercier, M. Drouin, Rares David, Bénédicte Chatelais, N. Duclos-Hindié, S. Roy
Fusing thermal and visible images is a recurring challenge in computer vision, especially when the images of the two modalities are not well registered. This registration problem is traditionally solved by matching descriptors and depends on the richness and discriminating power of the representation. Ensuring that detected features are dense and uniformly distributed is not necessarily guaranteed. More recently, machine learning methods addressed the issue of visible to visible matching, but few address the multi-modality setting. In this paper, we propose to address the special case of thermal-visible image registration with small baseline parallax correction. Our deep homography model is evaluated on an open thermal and visible dataset with two training settings, unsupervised and supervised. Results demonstrate the feasibility of the approach, and performances comparison to state-of-the-art models is evaluated.
{"title":"Thermal and Visible Image Registration Using Deep Homography","authors":"B. Debaque, Hughes Perreault, Jean-Philippe Mercier, M. Drouin, Rares David, Bénédicte Chatelais, N. Duclos-Hindié, S. Roy","doi":"10.23919/fusion49751.2022.9841256","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841256","url":null,"abstract":"Fusing thermal and visible images is a recurring challenge in computer vision, especially when the images of the two modalities are not well registered. This registration problem is traditionally solved by matching descriptors and depends on the richness and discriminating power of the representation. Ensuring that detected features are dense and uniformly distributed is not necessarily guaranteed. More recently, machine learning methods addressed the issue of visible to visible matching, but few address the multi-modality setting. In this paper, we propose to address the special case of thermal-visible image registration with small baseline parallax correction. Our deep homography model is evaluated on an open thermal and visible dataset with two training settings, unsupervised and supervised. Results demonstrate the feasibility of the approach, and performances comparison to state-of-the-art models is evaluated.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"29 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":"125860106","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/fusion49751.2022.9841361
P. Nell, A. D. Freitas, G. Pavlin, J. D. Villiers
In this paper, a method for ensuring the maintenance of multiple hypotheses in the presence of context data is proposed. In many practical context-based tracking problems where particle filtering is used, the filtering distribution is distinctly multimodal. Several of the state hypotheses may be lost owing to resampling of a finite number of particles, when the target leaves sensor coverage for several timesteps. This is especially the case where there is no sensor coverage in areas of the state space where particle density is low, and tracking is confined to narrow pathways, such as narrow roads and alleyways. The approach followed in this paper is to cluster particles into hypotheses using expectation maximisation of a multivariate Gaussian mixture, and to ensure that the number of particles per cluster is maintained using modified resampling. When no measurements are received for extended periods, two criteria are used to modify resampling to ensure hypothesis maintenance. This first adjusts resampling probabilities such that each hypothesis or cluster has roughly the same number of particles. The second adjusts resampling probabilities such that each hypothesis or cluster has a number of particles proportional to the narrowest dimension of the cluster (minimum eigenvalue of the cluster). This ensures that the particle density of each hypothesis remains roughly the same over all the hypotheses. The particular application will dictate which criterion is the most suitable.
{"title":"Particle-balanced context-based filtering for hypothesis maintenance in sparse sensor coverage situations","authors":"P. Nell, A. D. Freitas, G. Pavlin, J. D. Villiers","doi":"10.23919/fusion49751.2022.9841361","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841361","url":null,"abstract":"In this paper, a method for ensuring the maintenance of multiple hypotheses in the presence of context data is proposed. In many practical context-based tracking problems where particle filtering is used, the filtering distribution is distinctly multimodal. Several of the state hypotheses may be lost owing to resampling of a finite number of particles, when the target leaves sensor coverage for several timesteps. This is especially the case where there is no sensor coverage in areas of the state space where particle density is low, and tracking is confined to narrow pathways, such as narrow roads and alleyways. The approach followed in this paper is to cluster particles into hypotheses using expectation maximisation of a multivariate Gaussian mixture, and to ensure that the number of particles per cluster is maintained using modified resampling. When no measurements are received for extended periods, two criteria are used to modify resampling to ensure hypothesis maintenance. This first adjusts resampling probabilities such that each hypothesis or cluster has roughly the same number of particles. The second adjusts resampling probabilities such that each hypothesis or cluster has a number of particles proportional to the narrowest dimension of the cluster (minimum eigenvalue of the cluster). This ensures that the particle density of each hypothesis remains roughly the same over all the hypotheses. The particular application will dictate which criterion is the most suitable.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"60 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":"123455842","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/fusion49751.2022.9841305
Leah Strand, J. Honer, Alois Knoll
In this paper, we assess the performance of our real-world multi-camera traffic surveillance system along a segment of the A9 Autobahn north of Munich. Its principal component is a Labeled Multi-Bernoulli based tracking module that sequentially fuses the detection data from parallel camera processing pipelines. We present a systematic investigation of the system's characteristic failure modes that lead to a degradation of its performance. To this end, we assess state of the art metrics and performance measures in regard to their suitability for flagging unwanted behavior or failures in real-world multi-object tracking systems. Our analysis is structured into three levels of abstraction: target-level, time-step-level, and track-level. These abstraction levels allow us to systematically approach the analysis from different perspectives and to direct the focus on recurring errors and systemic deficiencies. In particular, the track-level analysis proved to be the most expedient approach since it drew our attention to system challenges like occlusions and other time-correlated detection errors. It further identified the system bias introduced by the adoption of class-dependent object extents. Our analysis is intended to guide the future development effort of our system and to serve as a basis for investigations and improvements of similar systems.
{"title":"Systematic Error Source Analysis of a Real-World Multi-Camera Traffic Surveillance System","authors":"Leah Strand, J. Honer, Alois Knoll","doi":"10.23919/fusion49751.2022.9841305","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841305","url":null,"abstract":"In this paper, we assess the performance of our real-world multi-camera traffic surveillance system along a segment of the A9 Autobahn north of Munich. Its principal component is a Labeled Multi-Bernoulli based tracking module that sequentially fuses the detection data from parallel camera processing pipelines. We present a systematic investigation of the system's characteristic failure modes that lead to a degradation of its performance. To this end, we assess state of the art metrics and performance measures in regard to their suitability for flagging unwanted behavior or failures in real-world multi-object tracking systems. Our analysis is structured into three levels of abstraction: target-level, time-step-level, and track-level. These abstraction levels allow us to systematically approach the analysis from different perspectives and to direct the focus on recurring errors and systemic deficiencies. In particular, the track-level analysis proved to be the most expedient approach since it drew our attention to system challenges like occlusions and other time-correlated detection errors. It further identified the system bias introduced by the adoption of class-dependent object extents. Our analysis is intended to guide the future development effort of our system and to serve as a basis for investigations and improvements of similar systems.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"121 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":"123690958","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/fusion49751.2022.9841314
K. Hauch, C. Redenbach
We define morphological operators and filters for directional images whose pixel values are vectors on the unit sphere. This requires an ordering relation for unit vectors which is obtained by using depth functions. They provide a centre-outward ordering with respect to a specified centre vector. We apply our operators on synthetic directional images and compare them with classical morphological operators for grey-scale images. As application example, we enhance the fault region in a compressed glass foam.
{"title":"Mathematical morphology on directional data","authors":"K. Hauch, C. Redenbach","doi":"10.23919/fusion49751.2022.9841314","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841314","url":null,"abstract":"We define morphological operators and filters for directional images whose pixel values are vectors on the unit sphere. This requires an ordering relation for unit vectors which is obtained by using depth functions. They provide a centre-outward ordering with respect to a specified centre vector. We apply our operators on synthetic directional images and compare them with classical morphological operators for grey-scale images. As application example, we enhance the fault region in a compressed glass foam.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"24 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":"114937341","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/fusion49751.2022.9841312
Patrick Hoher, Tim Baur, J. Reuter, F. Govaers, W. Koch
Multi-object tracking filters require a birth density to detect new objects from measurement data. If the initial positions of new objects are unknown, it may be useful to choose an adaptive birth density. In this paper, a circular birth density is proposed, which is placed like a band around the surveillance area. This allows for 360° coverage. The birth density is described in polar coordinates and considers all point-symmetric quantities such as radius, radial velocity and tangential velocity of objects entering the surveillance area. Since it is assumed that these quantities are unknown and may vary between different targets, detected trajectories, and in particular their initial states, are used to estimate the distribution of initial states. The adapted birth density is approximated as a Gaussian mixture, so that it can be used for filters operating on Cartesian coordinates.
{"title":"A Circular Detection Driven Adaptive Birth Density for Multi-Object Tracking with Sets of Trajectories","authors":"Patrick Hoher, Tim Baur, J. Reuter, F. Govaers, W. Koch","doi":"10.23919/fusion49751.2022.9841312","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841312","url":null,"abstract":"Multi-object tracking filters require a birth density to detect new objects from measurement data. If the initial positions of new objects are unknown, it may be useful to choose an adaptive birth density. In this paper, a circular birth density is proposed, which is placed like a band around the surveillance area. This allows for 360° coverage. The birth density is described in polar coordinates and considers all point-symmetric quantities such as radius, radial velocity and tangential velocity of objects entering the surveillance area. Since it is assumed that these quantities are unknown and may vary between different targets, detected trajectories, and in particular their initial states, are used to estimate the distribution of initial states. The adapted birth density is approximated as a Gaussian mixture, so that it can be used for filters operating on Cartesian coordinates.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"53 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":"122732731","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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