Implementation of Wireless Vision Sensor Node for Characterization of Particles in Fluids

Imran, Muhammad; Khursheed, Khursheed; Lawal, Najeem; O’Nils, Mattias; Ahmad, Naeem

doi:10.1109/tcsvt.2012.2202189

Cited by 18 publications

(31 citation statements)

References 20 publications

(21 reference statements)

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“…The resource utilization in terms of slice LUTs is less than 1 percent. This means that post background subtraction techniques such as segmentation, morphology, labelling and compression schemes can be easily accommodated using the proposed subtraction technique, if we consider the resource requirement of the mentioned tasks for current reconfigurable devices [21]. The power consumption of the proposed background subtraction technique was calculated by using Xilinx Xpower analyzer tool [16] and is shown in Table 2.…”

Section: Performance Comparisonmentioning

confidence: 99%

Low complexity FPGA based background subtraction technique for thermal imagery

Imran

O’Nils

Munir

et al. 2015

Proceedings of the 9th International Conference on Distributed Smart Cameras

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Embedded smart camera systems are gaining popularity for a number of real world surveillance applications. However, there are still challenges, i.e. variation in illumination, shadows, occlusion, and weather conditions while employing the vision algorithms in outdoor environments. For safety-critical surveillance applications, the visual sensors can be complemented with beyond-visual-range sensors. This in turn requires analysis, development and modification of existing imaging techniques. In this work, a low complexity background modelling and subtraction technique has been proposed for thermal imagery. The proposed technique has been implemented on Field Programmable Gate Arrays (FPGAs) after in-depth analysis of different sets of images, characterizing poor signal-to-noise ratio challenges, e.g. motion of high frequency background objects, temperature variation and camera jitter etc. The proposed technique dynamically updates the background on pixel level and requires a single frame storage as opposed to existing techniques. The comparison of this approach with two other approaches show that this approach performs better in different environmental conditions. The proposed technique has been modelled in Register Transfer Logic (RTL) and implementation on the latest FPGAs shows that the design requires less than 1 percent logics, 47 percent block RAMs, and consumes 91 mW power consumption on Artix-7 100T FPGA.

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Section: Performance Comparisonmentioning

confidence: 99%

Low complexity FPGA based background subtraction technique for thermal imagery

Imran

O’Nils

Munir

et al. 2015

Proceedings of the 9th International Conference on Distributed Smart Cameras

Self Cite

View full text Add to dashboard Cite

show abstract

“…The recent advancement in technology has enabled the WVSN to be used for a number of potential applications including machine vision [2], Manuscript received Jan. 20,2013. This work was supported by STC@MIUN Research Program, Mid Sweden University, Sweden.…”

Section: Introductionmentioning

confidence: 99%

“…WVSNs are often comprised of many individual wireless Vision Sensor Nodes (VSNs) which can capture and process data. Depending on the requirements, a VSN can make decisions [6], or transmit data to a user/server for further analysis [2][5] [9]. As compared to traditional vision monitoring systems, which use wired communication and wall power supply, VSNs are interconnected with each other by using a wireless link.…”

Section: Introductionmentioning

confidence: 99%

“…In order to extend the lifetime of a VSN, researchers generally employ two strategies [2][3] [4][5] [9] [13]. In the first strategy, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…This strategy consumes a smaller communication energy. However, it consumes a greater processing energy on the currently available software platforms and has a high design complexity with regards to the hardware platforms [2].…”

Section: Introductionmentioning

confidence: 99%

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