Modeling depth uncertainty of desynchronized multi-camera systems

Dima, Elijs; Sjöström, Mårten; Olsson, Roger

doi:10.1109/ic3d.2017.8251891

Cited by 3 publications

(3 citation statements)

References 32 publications

(34 reference statements)

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“…The implementation supports both hardware and software synchronization. This enables to simulate different applications and the consequence synchronization errors have on data consistency [8]. We use a signal generator to provide a synchronization signal to the Basler cameras, and the GPCs are software-synchronized using Network Time Protocol.…”

Section: Gateway Cameramentioning

confidence: 99%

Life: A Flexible Testbed for Light Field Evaluation

Dima

Sjöström

Olsson

et al. 2018

2018 - 3dtv-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3dtv-Con)

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Recording and imaging the 3D world has led to the use of light fields. Capturing, distributing and presenting light field data is challenging, and requires an evaluation platform. We define a framework for real-time processing, and present the design and implementation of a light field evaluation system. In order to serve as a testbed, the system is designed to be flexible, scalable, and able to model various end-to-end light field systems. This flexibility is achieved by encapsulating processes and devices in discrete framework systems. The modular capture system supports multiple camera types, general-purpose data processing, and streaming to network interfaces. The cloud system allows for parallel transcoding and distribution of streams. The presentation system encapsulates rendering and display specifics. The real-time ability was tested in a latency measurement; the capture and presentation systems process and stream frames within a 40 ms limit.

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Section: Gateway Cameramentioning

confidence: 99%

Life: A Flexible Testbed for Light Field Evaluation

Dima

Sjöström

Olsson

et al. 2018

2018 - 3dtv-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3dtv-Con)

Self Cite

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“…Existing research purely based on images [15,[18][19][20][21] either does not achieve the temporal precision required by BSV or relies on features unsuitable for crowd scenes characterized by significant occlusions. Furthermore, when C3 is unknown or fraught with large errors [22], C1 and C2 become indefinable. Only a handful of research endeavors have proposed joint solutions for C2 and C3 when dealing with individual pedestrians [23].…”

Section: Introductionmentioning

confidence: 99%

Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes

Yang,

Guo

2023

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The study of crowd movement and behavioral patterns typically relies on spatio-temporal localization data of pedestrians. While monocular cameras serve the purpose, industrial binocular cameras based on multi-view geometry offer heightened spatial accuracy. These cameras synchronize time through circuits and are calibrated for external parameters after fixing their relative positions. Yet, the flexibility and real-time adaptability of using two different cameras or smartphones in close proximity, forming a short-baseline binocular camera, presents challenges in camera time synchronization, external parameter calibration, and pedestrian feature matching. A method is introduced herein for jointly addressing these challenges. Images are abstracted into spatial-temporal point sets based on human head coordinates and frame numbers. Through point set registration, time synchronization and pedestrian matching are achieved concurrently, followed by the calibration of the shortbaseline camera's external parameters. Numerical results from synthetic and real-world scenarios indicate the proposed model's capability in addressing the aforementioned fundamental challenges. With the sole reliance on crowd image data, devoid of external hardware, software, or manual calibrations, time synchronization precision reaches the submillisecond level, pedestrian matching averages a 92% accuracy rate, and the camera's external parameters align with the calibration board's precision. Ultimately, this research facilitates the self-calibration, automatic time synchronization, and pedestrian matching tasks for short-baseline camera assemblies observing crowds.

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“…Another fundamental problem in light field processing is to estimate the depth map, and it serves as a prerequisite for a variety of applications, ranging from virtual reality [5]- [7] to microscopy imaging [8], [9]. Though numerous depth estimation approaches have been proposed over the last decade, for example, stereo matching [10]- [13] and shape from focus [8], [14], [15], it is worth noting that the photo-consistency condition holds for all of them.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Pla

Sjöström

et al. 2022

IEEE Access

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Recent studies in the light field imaging have shown the potential and advantages of different light field information processes. In most of the existing techniques, the processing pipeline of light field has been treated in a step-by-step manner, and each step is considered to be independent from the others. For example, in light field color demosaicing, inferring the scene geometry is treated as an irrelevant and negligible task, and vice versa. Such processing techniques may fail due to the inherent connection among different steps, and result in both corrupted post-processing and defective pre-processing results.In this paper, we address the interaction between color interpolation and depth estimation in light field, and propose a probabilistic approach to handle these two processing steps jointly. This probabilistic framework is based on a Markov Random Fields -Collaborative Graph Model for simultaneous Demosaicing and Depth Estimation (CGMDD)-to explore the color-depth interdependence from general light field sampling. Experimental results show that both image interpolation quality and depth estimation can benefit from their interaction, mainly for processes such as image demosaicing which are shown to be sensitive to depth information, especially for light field sampling with large baselines.INDEX TERMS Light field, demosaicing, depth estimation, Markov random field, graph model.

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Modeling depth uncertainty of desynchronized multi-camera systems

Cited by 3 publications

References 32 publications

Life: A Flexible Testbed for Light Field Evaluation

Life: A Flexible Testbed for Light Field Evaluation

Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

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