Shape from incomplete silhouettes based on the reprojection error

Haro, Gloria; Pardís, Montse

doi:10.1016/j.imavis.2010.01.016

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…In D1 and D2, although the tracker is able to correctly assign the ids, there exist occasions where lack of visibility produces false-positive detections (assignment of a tracking hypothesis to a non-existing person in the scene), for both methods. These false-positives are mainly due to a volumetric artifact occurring at the dynamic occlusion of an area, which has been studied in [5]. The increased complexity of D3 results in lower MOTA values for this dataset, for both methods.…”

Section: Methodsmentioning

confidence: 81%

Multicamera tracking of multiple humans based on colored visual hulls

Padeleris

Zabulis

Argyros

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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Detecting, localizing and tracking humans within an industrial environment are three tasks which are of central importance towards achieving automation in workplaces and intelligent environments. This is because unobtrusive, real-time and reliable person tracking provides valuable input to solving problems such as workplace surveillance and event/activity recognition and, also, contributes to safety and optimized use of resources. This paper presents a passive approach to the problem of person tracking that is based on a network of conventional color cameras. The proposed approach exhibits robustness to challenging conditions that are encountered in industrial environments due to illumination artifacts, occlusions and the highly dynamic nature of the observed scenes. The multiple views of the environment that the system employs are used to obtain a volumetric representation of the humans within it, in real-time. Although human tracking can be achieved based solely on such a volumetric representation, in demanding scenes, this information is not enough to recover from tracking failures. Thus, in this work, we collect and update a representation of the color appearance of the persons in the environment. The combination of volumetric and color information reinforces tracking robustness, even when a person is not visible by any of the cameras for extended time intervals. The proposed approach has been extensively evaluated in comparison with an existing state of the art method and pertinent results are reported.

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Section: Methodsmentioning

confidence: 81%

Multicamera tracking of multiple humans based on colored visual hulls

Padeleris

Zabulis

Argyros

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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“…Our work is most closely aligned with that of Haro and Pardás [13]. Their approach is to minimize an approximation of the silhouette inconsistency error, where the function used depends on whether the SfIS or SfSPM problem is being considered.…”

Section: Related Workmentioning

confidence: 99%

Shape from Silhouette Probability Maps: Reconstruction of Thin Objects in the Presence of Silhouette Extraction and Calibration Error

Tabb

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…We use the SfIS reconstruction method described in [14]. The camera calibration correction does not depend on the choice of the reconstruction method, though, so other SfIS reconstruction methods may be substituted such as [15], [16], [17], [18], and [19].…”

Section: Introductionmentioning

confidence: 99%

Camera calibration correction in Shape from Inconsistent Silhouette

Tabb

Park

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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The use of shape from silhouette for reconstruction tasks is plagued by two types of realworld errors: camera calibration error and silhouette segmentation error. When either error is present, we call the problem the Shape from Inconsistent Silhouette (SfIS) problem. In this paper, we show how small camera calibration error can be corrected when using a previously-published SfIS technique to generate a reconstruction, by using an Iterative Closest Point (ICP) approach. We give formulations under two scenarios: the first of which is only external camera calibration parameters rotation and translation need to be corrected for each camera and the second of which is that both internal and external parameters need to be corrected. We formulate the problem as a 2D-3D ICP problem and find approximate solutions using a nonlinear minimization algorithm, the Levenberg-Marquadt method. We demonstrate the ability of our algorithm to create more representative reconstructions of both synthetic and real datasets of thin objects as compared to uncorrected datasets.

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Shape from incomplete silhouettes based on the reprojection error

Cited by 17 publications

References 22 publications

Multicamera tracking of multiple humans based on colored visual hulls

Multicamera tracking of multiple humans based on colored visual hulls

Shape from Silhouette Probability Maps: Reconstruction of Thin Objects in the Presence of Silhouette Extraction and Calibration Error

Camera calibration correction in Shape from Inconsistent Silhouette

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