Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking - 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. (Sensors (Basel, Switzerland), 2022)
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.