Distributed and Context Aware Application of Deep Neural Networks in Mobile 3D-Multi-sensor Systems Based on Cloud-, Edge- and FPGA-Computing

Engel, Grischan; Bhatti, Faraz; Greiner, Thomas; Heizmann, Michael; Quint, Franz

doi:10.1109/iciea49774.2020.9101915

Cited by 2 publications

(3 citation statements)

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“…During the stereo-matching process, to find the correspondence, I r needs to be correlated with several I ti which are placed at b distance from the respective I r . Whereby b is calculated by Equation (10). The values inscribed in the micro-lenses refer to the l n of the respective baseline distance.…”

Section: Temporal Parallelizationmentioning

confidence: 99%

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Design of an FPGA Hardware Optimizing the Performance and Power Consumption of a Plenoptic Camera Depth Estimation Algorithm

Bhatti¹,

Greiner²

2021

Algorithms

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Plenoptic camera based system captures the light-field that can be exploited to estimate the 3D depth of the scene. This process generally consists of a significant number of recurrent operations, and thus requires high computation power. General purpose processor based system, due to its sequential architecture, consequently results in the problem of large execution time. A desktop graphics processing unit (GPU) can be employed to resolve this problem. However, it is an expensive solution with respect to power consumption and therefore cannot be used in mobile applications with low energy requirements. In this paper, we propose a modified plenoptic depth estimation algorithm that works on a single frame recorded by the camera and respective FPGA based hardware design. For this purpose, the algorithm is modified for parallelization and pipelining. In combination with efficient memory access, the results show good performance and lower power consumption compared to other systems.

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Section: Temporal Parallelizationmentioning

confidence: 99%

“…The target mobile application is an assisted wheeled walker for the elderly. It is equipped with a plenoptic camera system to estimate the depth of the objects in realtime [10]. The objective is to warn or alert the respective user for an imminent hazardous situation or objects.…”

Section: Introductionmentioning

confidence: 99%

Design of an FPGA Hardware Optimizing the Performance and Power Consumption of a Plenoptic Camera Depth Estimation Algorithm

Bhatti¹,

Greiner²

2021

Algorithms

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“…The target application is a mobile system, concretely, an assisted wheeled walker for the elderly. It is equipped with the plenoptic camera system to estimate the depth of the objects in real time [5]. General purpose processor based solution results in a large execution time because of its sequential nature.…”

Section: Introductionmentioning

confidence: 99%

FPGA Hardware Design for Plenoptic 3D Image Processing Algorithm Targeting a Mobile Application

Bhatti

Greiner

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Over the past years, widespread use of applications based on 3D image processing has increased rapidly. It is being employed in various fields, such as research, medicine and automation. Plenoptic camera system is used to capture lightfield that can be exploited to estimate the 3D depth of the scene. The respective algorithms consist of a large number of computation-intensive instructions. It eventually leads to the problem of large execution time of the algorithm. Moreover, they require substantial amount of memory cells for the storage of intermediate and final results. Desktop GPU based solutions are power intensive and therefore cannot be used in the mobile applications with low energy requirements. The idea presented in this paper is to use the FPGA based hardware design to improve the performance of a 3D depth estimation algorithm by utilizing the advantage of concurrent execution. The algorithm is implemented, evaluated and the results show that FPGA design reduces the respective execution time significantly.

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Distributed and Context Aware Application of Deep Neural Networks in Mobile 3D-Multi-sensor Systems Based on Cloud-, Edge- and FPGA-Computing

Cited by 2 publications

References 32 publications

Design of an FPGA Hardware Optimizing the Performance and Power Consumption of a Plenoptic Camera Depth Estimation Algorithm

Design of an FPGA Hardware Optimizing the Performance and Power Consumption of a Plenoptic Camera Depth Estimation Algorithm

FPGA Hardware Design for Plenoptic 3D Image Processing Algorithm Targeting a Mobile Application

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