An optimized vision library approach for embedded systems

Dedeoǧlu, Göksel; Kisačanin, Branislav; Moore, Darnell; Sharma, Vinay; Miller, Andrew

doi:10.1109/cvprw.2011.5981731

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Generally, optimization techniques on embedded can be summarized to several categories such as compiler optimization, source code modification, memory optimization and hardware-level optimization (ARM, 2014). Several pieces of research focused on optimization of the developed system that utilizes the embedded multi-core system (Ma & Wang, 2016), memory management (Muck & Frohlich, 2011;Lei & Xiao-ya, 2011), hardware and system-level optimization (Dekkiche et al, 2016;Dedeoğlu et al, 2011;Singhal et al, 2012) and software optimization (Park et al, 2013;Joshi & Gurumurthy, 2014).…”

Section: Related Workmentioning

confidence: 99%

Software Optimization of Vision-Based Around View Monitoring System on Embedded Platform

Mahamud

Abidin²,

Zaki³

et al. 2020

JSAEM

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Image processing algorithm requires high computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7.

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Section: Related Workmentioning

confidence: 99%

Software Optimization of Vision-Based Around View Monitoring System on Embedded Platform

Mahamud

Abidin²,

Zaki³

et al. 2020

JSAEM

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“…The implementation of computer vision applications in automotive environments is not straightforward because several requirements must be taken into account: reliability [212,213], real-time performance [213,214,215], low-cost [216,217,218,219], spatial constraints [217,219], low power consumption [220], flexibility [219], rapid prototyping [215,221], design requirements [217] and short time to market [217]. Therefore, there must be a trade-off among these design requisites [217].…”

Section: Sensorsmentioning

confidence: 99%

“…DSPs have been used in image and audio signal processing when the use of microcontrollers was not enough. These processors were used in [215], where an optimized vision library approach for embedded systems was presented. VLIB is a software library that accelerates computer vision applications for high-performance embedded systems.…”

Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…Three main challenges are currently considered as bottlenecks in the embedded vision pipeline: the growing diversity of camera hardware, the relatively limited computational resources of embedded and real-time platforms, and the execution of vision-based decisions, due to the required integration with other components, such as sensors and actuators Dedeoğlu et al, 2011). Obviously, the underlying challenge of each bottleneck is standardization.…”

Section: Introductionmentioning

confidence: 99%

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

Heimlich

Tsur

2016

Front. ICT

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Embedded real-time vision applications are being rapidly deployed in a large realm of consumer electronics, ranging from automotive safety to surveillance systems. However, the relatively limited computational power of embedded platforms is considered as a bottleneck for many vision applications, necessitating optimization. OpenVX is a standardized interface, released in late 2014, in an attempt to provide both system and kernel level optimization to vision applications. With OpenVX, Vision processing is modeled with coarse-grained data flow graphs, which can be optimized and accelerated by the platform implementer. Current full implementations of OpenVX are given in the programming language C, which neither supports advanced programming paradigms, such as object-oriented, imperative, and functional programming, nor does it has runtime or type checking. Here, we present a python-based full Implementation of OpenVX, which eliminates much of the discrepancies between the object-oriented paradigm used by many modern applications and the native C implementations. Our open-source implementation can be used for rapid development of OpenVX applications in embedded platforms. Demonstration includes static and real-time image acquisition and processing using a Raspberry Pi and a GoPro camera. Code is given in Supplementary Material. Code project and linked deployable virtual machine are located on GitHub: https://github.com/ NBEL-lab/PythonOpenVX.

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An optimized vision library approach for embedded systems

Cited by 8 publications

References 7 publications

Software Optimization of Vision-Based Around View Monitoring System on Embedded Platform

Software Optimization of Vision-Based Around View Monitoring System on Embedded Platform

Driver Distraction Using Visual-Based Sensors and Algorithms

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

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