ASIC implementation of a Gaussian Pyramid for use in autonomous mobile robotics

Mielke, Matthias; Schafer, Andre; Brück, Rainer

doi:10.1109/mwscas.2011.6026563

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…For example, several well-known algorithms have been used and optimized for this field of application, such as: (a) spatial and temporal filtering, motion detection and optical flow analysis [229] or (b) gray scale projection, edge detection with Prewitt operator and complexity functions [230]. Additionally, the use of Application-Specific Integrated Circuits (ASIC), which is an IC customized for a particular use rather than intended for general-purpose use, has also been considered [233]. FPGAs have an important advantage over ASICs: they are reconfigurable, which gives them some of the flexibility of software.…”

Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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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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Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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show abstract

“…This lack of flexibility has led to the use of other alternatives such as field-programmable gate arrays (FPGAs). However, some examples of ADAS implementations in ASIC can still be found in the literature [10,11]. This technology was also used by Mobileye to build its products EyeQ [12] and EyeQ2 [13], which are composed of dual central processing unit (CPU) cores running in parallel with multiple additional dedicated and programmable cores.…”

Section: Application-specific Integrated Circuit (Asic)mentioning

confidence: 99%

Embedding vision‐based advanced driver assistance systems: a survey

Vélez

Otaegui

2016

IET Intelligent Transport Systems

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“…In addition, they have another major drawback: they are not reconfigurable. During the last decade, a large number of implementations have appeared both ASIC [8, 9] and FPGA [10–14].…”

Section: Related Workmentioning

confidence: 99%