Exploration of Tasks Partitioning between Hardware Software and Locality for a Wireless Camera Based Vision Sensor Node

IEEE Trans. Circuits Syst. Video Technol.

Lawal

et al. 2012

Self Cite

Section: Analysis Of Vsn On Software and Hardware-software Platformmentioning

confidence: 84%

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

IEEE Trans. Circuits Syst. Video Technol.

Lawal

et al. 2012

Self Cite

“…Uniprocessors such as embedded processors are commonly employed for vision processing because of the availability of ready-to-use libraries. In our previous work [17], a system has been implemented on both software and hardware platforms.…”

Section: Postprocessing (G)mentioning

confidence: 99%

Complexity Analysis of Vision Functions for Comparison of Wireless Smart Cameras

International Journal of Distributed Sensor Networks

Ahmad

et al. 2014

Self Cite

There are a number of challenges caused by the large amount of data and limited resources such as memory, processing capability, energy consumption, and bandwidth, when implementing vision systems on wireless smart cameras using embedded platforms. It is usual for research in this field to focus on the development of a specific solution for a particular problem. There is a requirement for a tool which facilitates the complexity estimation and comparison of wireless smart camera systems in order to develop efficient generic solutions. To develop such a tool, we have presented, in this paper, a complexity model by using a system taxonomy. In this model, we have investigated the arithmetic complexity and memory requirements of vision functions with the help of system taxonomy. To demonstrate the use of the proposed model, a number of actual systems are analyzed in a case study. The complexity model, together with system taxonomy, is used for the complexity estimation of vision functions and for a comparison of vision systems. After comparison, the systems are evaluated for implementation on a single generic architecture. The proposed approach will assist researchers in benchmarking and will assist in proposing efficient generic solutions for the same class of problems with reduced design and development costs.

“…1 for the software implementation (based on SENTIO32) and hardware implementation (based on FPGA) of the VSN and presented the results in [4] and [5] respectively. The interested readers are referred to [4], [5] for an explanation and for the usage of the image processing tasks shown in Fig. 1 as well as for a detailed discussion of the intelligence partitioning strategies.…”

Section: Suitable Intelligence Partitioning Strategiesmentioning

confidence: 99%

The Effect of Packets Relaying on the Implementation Issues of the Visual Sensor Node

Ahmad

et al. 2013

ElAEE

Wireless Visual Sensor Networks (WVSNs) are used for the monitoring of large and inaccessible areas. WVSNs are feasible today due to the advancement in many fields of electronics such as CMOS cameras, low power computing platforms, distributed computing and radio transceivers. The energy budget in a WVSN is limited because of the wireless nature of the applications and the small physical size of the Visual Sensor Node (VSN). The WVSN covers a large area where every node cannot transmit its results directly to the server. Receiving and forwarding other node's packets consumes a large portion of the energy budget of the VSNs. This paper explores the effect of packets relaying in a multihop WVSN on the implementation issues of the VSN. It also explores the effect of node density in the multihop WVSN on the energy consumption, which in turn, has an impact on the lifetime of the VSN. Results show that the network topology does not affect the software implementation of the VSN because of the relatively high execution time of the image processing tasks on the microcontroller. For hardware implementation, network topology and node density does affect the architecture of the VSN due to the fact that communication energy consumption is dominant (because of the low execution time on FPGAs).The WVSNs are suitable for applications having a limited availability of power and in those cases where it is inconvenient to modify the locations of the VSNs or to frequently change the batteries. The large amount of data generated at VSN requires a great deal of energy for processing at the VSN and communication to server.Both on-board processing and communication influence the energy consumption of the VSN. In the literature, many authors have focused on two implementation strategies for performing image processing tasks either at the VSN or at the server. Some authors have designed a VSN which transmits the raw compressed images to the server for further processing [1]. In this case, the communication energy consumption is high because of the transmission of large chunks of raw compressed data.On the other hand, some authors have considered performing all the image processing tasks at the VSN and to transmit the final results to the server. In this case, the communication energy consumption is low but the computational energy consumption is high because of the longer processing time of the VSN. An example, representing all the computation at the VSN, is presented in [2] where the authors implemented a distributed vision processing system for human pose interpretation on a wireless smart camera network. They discussed that the motivation behind employing distributed processing is to process the data in real-time and to also provide scalability for developing more complex vision algorithms. By performing local processing at the smart camera, they extracted critical joints of the subject in the scene in real time. The results obtained by multiple smart cameras are then transmitted wirelessly to a server for the reconstruction of the huma...