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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 1280
• Main Authors: Soubervielle-Montalvo, Carlos, Perez-Cham, Oscar E., Puente, Cesar, Gonzalez-Galvan, Emilio J., Olague, Gustavo, Aguirre-Salado, Carlos A., Cuevas-Tello, Juan C., Ontanon-Garcia, Luis J.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2022; MDPI
• Subjects: Accuracy; Algorithms; Animals; Automation; Bees; Computer vision; Digital integrated circuits; Digital signal processors; Embedded systems; evolutionary computing; Field programmable gate arrays; field-programmable gate array; heterogeneous computing; Heuristic; Honeybee; Intelligence; Linear algebra; Machine learning; Machine vision; meta-heuristic; Multiprocessing; Optimization; Proposals; Robotics; Semiconductor industry; Similarity measures; Software; Swarm intelligence; system-on-chip; video tracking; United States; United Kingdom; Germany; embedded system design; meta-heuristic; heterogeneous computing; evolutionary computing; computer vision; graphics processing unit; video tracking; field-programmable gate array; system-on-chip; swarm intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22031280

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.