3D Extended Object Tracking Using Recursive Gaussian Processes

In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can jointly estimate the position, orientation, and the shape of the object. The inference is performed by an extended Kalman filter which is suitable for online real-time applications. Lastly, we demonstrate the initial results of a promising approach, which aims at reducing the computational complexity.


New models and inference techniques for Gaussian process-based extended object tracking
Kumru, Murat; Özkan, Emre; Department of Electrical and Electronics Engineering (2022-9-09)
In this thesis, we consider the problem of tracking dynamic objects with unknown shapes using point cloud measurements generated by, e.g., lidars, radars, and depth cameras. The point measurements do not only convey information about the object pose, i.e., position and orientation, but they also naturally reveal the characteristics of its latent extent. Aiming to harness the full potential of the available information, we investigate the Gaussian process-based extended object tracking (GPEOT) framework. W...
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In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...
Multi-target tracking using passive doppler measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
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Scale invariant representation of 2 5D data
AKAGUNDUZ, Erdem; ULUSOY PARNAS, İLKAY; BOZKURT, Nesli; Halıcı, Uğur (2007-06-13)
In this paper, a scale and orientation invariant feature representation for 2.5D objects is introduced, which may be used to classify, detect and recognize objects even under the cases of cluttering and/or occlusion. With this representation a 2.5D object is defined by an attributed graph structure, in which the nodes are the pit and peak regions on the surface. The attributes of the graph are the scales, positions and the normals of these pits and peaks. In order to detect these regions a "peakness" (or pi...
Citation Formats
M. Kumru and E. Özkan, “3D Extended Object Tracking Using Recursive Gaussian Processes,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34474.