ColourFAST: GPU-based feature point detection and tracking on mobile devices

Ensor, Andrew; Hall, Seth

doi:10.1109/ivcnz.2013.6727003

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…As outlined in Section 2, on-device processing suffers from lower semantic performance, low availability of tools/libraries, and risk of overheating causing damage to the device. The off-device approach, on the other hand, requires access to a mobile or wireless network, which may result in latency and/or usage charges [6], and can also raise security questions.…”

Section: Xqm Architecturementioning

confidence: 99%

XQM: Interactive Learning on Mobile Phones

Bagi

Schild

Khan

et al. 2021

MultiMedia Modeling

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There is an increasing need for intelligent interaction with media collections, and mobile phones are gaining significant traction as the device of choice for many users. In this paper, we present XQM, a mobile approach for intelligent interaction with the user's media on the phone, tackling the inherent challenges of the highly dynamic nature of mobile media collections and limited computational resources of the mobile device. We employ interactive learning, a method that conducts interaction rounds with the user, each consisting of the system suggesting relevant images based on its current model, the user providing relevance labels, the system's model retraining itself based on these labels, and the system obtaining a new set of suggestions for the next round. This method is suitable for the dynamic nature of mobile media collections and the limited computational resources. We show that XQM, a full-fledged app implemented for Android, operates on 10K image collections in interactive time (less than 1.4 seconds per interaction round), and evaluate user experience in a user study that confirms XQM's effectiveness.

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Section: Xqm Architecturementioning

confidence: 99%

XQM: Interactive Learning on Mobile Phones

Bagi

Schild

Khan

et al. 2021

MultiMedia Modeling

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“…Driven by the insatiable demand for 3D games and applications, handheld devices have evolved into highly parallel heterogeneous platforms with tremendous computational power. A rapidly growing and virtually ubiquitous mobile phone technology field is giving rise to many applications in the fields of robotics, control and image processing [19]. Complex control algorithms involving trigonometric equations, transformations matrices and their computations can be accelerated using concurrency of GPGPUs.…”

Section: Introductionmentioning

confidence: 99%

“…Use of these handheld mobile accelerators can replace multiple dedicated computation devices by a single processor in motion controllers of robotic systems, which can eventually enable the manipulators to execute massive complex tasks with smaller physical dimensions. Moreover, modern handheld devices normally have a 1-GHz CPU and a GPU such as Adreno (Qualcomm, San Diego, CA, USA, formerly of AMD), PowerVR SGX (Imagination Technologies, Kings Langley, UK), Mali (ARM) or Tegra 2 (NVIDIA) [19,26,27]. These findings motivated us to utilize the handheld mobile GPU as a primary processor to offload the computational burden of a numeric controller and achieve significant acceleration in performance with minimum power consumption.…”

Section: Introductionmentioning

confidence: 99%

A General-Purpose Graphics Processing Unit (GPGPU)-Accelerated Robotic Controller Using a Low Power Mobile Platform

Rizvi

Cabodi

Patti

et al. 2017

JLPEA

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Robotic controllers have to execute various complex independent tasks repeatedly. Massive processing power is required by the motion controllers to compute the solution of these computationally intensive algorithms. General-purpose graphics processing unit (GPGPU)-enabled mobile phones can be leveraged for acceleration of these motion controllers. Embedded GPUs can replace several dedicated computing boards by a single powerful and less power-consuming GPU. In this paper, the inverse kinematic algorithm based numeric controllers is proposed and realized using the GPGPU of a handheld mobile device. This work is the extension of a desktop GPU-accelerated robotic controller presented at DAS'16 where the comparative analysis of different sequential and concurrent controllers is discussed. First of all, the inverse kinematic algorithm is sequentially realized using Arduino-Due microcontroller and the field-programmable gate array (FPGA) is used for its parallel implementation. Execution speeds of these controllers are compared with two different GPGPU architectures (Nvidia Quadro K2200 and Nvidia Shield K1 Tablet), programmed with Compute Unified Device Architecture (CUDA) computing language. Experimental data shows that the proposed mobile platform-based scheme outperforms the FPGA by 5× and boasts a 100× speedup over the Arduino-based sequential implementation.

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