A FPGA-based accelerated architecture for the Continuous GRASP

Nogueira, Bruno; Barboza, Erick de A.

doi:10.1007/s00607-020-00850-5

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the video tracking community focuses on accuracy issues, other factors such as video tracker speed must also be considered [ 15 ]. For example, some studies in the literature have used tools such as heterogeneous computing architectures and population-based meta-heuristics to improve efficiency rather than efficacy [ 16 , 17 ]. A heterogeneous computing system is any computing system that coordinates two or more different types of processing elements.…”

Section: Introductionmentioning

confidence: 99%

“…The work of Morsi et al [ 16 ] is particularly interesting because the application is video tracking, but the work uses only FPGA. Another interesting work is that of Nogueira and Barboza [ 17 ] because it studies SoC-FPGA and CPU-GPU heterogeneous computing platforms; however, the application is not specific as it focuses on continuous optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking

Soubervielle‐Montalvo

Perez-Cham

Puente

et al. 2022

Sensors

View full text Add to dashboard Cite

Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%