A Generalized Labeled Multi-Bernoulli Filter With Object Spawning

Bryant, Daniel S.; Vo, Ba-Ngu; Jones, Brandon A.

doi:10.1109/tsp.2018.2872856

Cited by 50 publications

(30 citation statements)

References 37 publications

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“…We chose a resolution of 768 × 1024 × 21 resulting in approximately 0.08m×0.08m×0.19m per cell. Each voxel, where at least one point exists inside its 3D space and is visible to the front camera, is filled with a normalized class value extracted from the semantic map in range [1,2]. Therefore, we project all relevant points into the image using calibrations from [8] and argmax over the frequency of all resolved classes.…”

Section: Point Cloud Preprocessingmentioning

confidence: 99%

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Simón

Amende

Kraus

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

189

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Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics. In this work we present a novel fusion of neural network based state-of-the-art 3D detector and visual semantic segmentation in the context of autonomous driving. Additionally, we introduce Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable evaluation metric for comparison of object detections, which speeds up our inference time up to 20% and halves training time. On top, we apply state-of-the-art online multi target feature tracking on the object measurements to further increase accuracy and robustness utilizing temporal information. Our experiments on KITTI show that we achieve same results as state-of-the-art in all related categories, while maintaining the performance and accuracy trade-off and still run in real-time. Furthermore, our model is the first one that fuses visual semantic with 3D object detection.

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Section: Point Cloud Preprocessingmentioning

confidence: 99%

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Simón

Amende

Kraus

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

189

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show abstract

“…. Different from the δ-GLMB spawning model of the point target in reference [30], neither states of the split subgroups are identical with the expected value of its parent in this paper. That is to say, the split subgroups and its parent are mutually exclusive; e.g., when the formation of the UAV cluster is split into two subgroups, the group extension and measurement rate are greatly changed.…”

Section: Ggiw-δ-glmb Prediction and Update With Splittingmentioning

confidence: 79%

“…Consider the splitting model in references [18,30], we make the following assumptions: A1: The group target splits at the prediction phase. A2: Let the dimension of the extension state be d. As the split may happen in any dimension, if a parent track with label l ∈ L at time k splits 2 subgroups (split pair) at time k + 1, then, each subgroup takes on the label l T,i,j = l, k + 1, i, j , while i = 1, … , d, j = 1, 2, and the parameter i is the same for the split pair.…”

Section: Ggiw-δ-glmb Prediction and Update With Splittingmentioning

confidence: 99%

“…That is to say, the split subgroups and its parent are mutually exclusive; e.g., when the formation of the UAV cluster is split into two subgroups, the group extension and measurement rate are greatly changed. While in reference [30], one of the split subgroups has an identical expected value on state to its parent e.g., after launching the missle, the extended state of the aircraft is almost unchanged. ② In reference [30], the split and the survival of the track are in parataxis; there are 3 possible states of a current target at the next time: survival, death, and split.…”

Section: Ggiw-δ-glmb Prediction and Update With Splittingmentioning

confidence: 99%

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Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter

Linhai

Wang

2019

Journal of Sensors

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As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.

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“…GLMB filtering has been demonstrated to track more than one million targets in heavy clutter, misdetections and data association uncertainty [37]. Another advantage of labeled RFS over unlabeled RFS is that it can provide ancestry or lineage information in problems that involve spawning targets [38]. Such capability is not possible without labels.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

Tian¹,

Liu²,

Ru³

2019

Sensors

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This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.

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A Generalized Labeled Multi-Bernoulli Filter With Object Spawning

Cited by 50 publications

References 37 publications

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter

Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

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