Mobile and embedded devices are becoming ubiquitous. Applications such as rescue with autonomous robots and event analysis on traffic cameras rely on devices with limited power supply and computational sources. Thus, the demand for efficient computer vision algorithms increases. Since 2015, we have organized the IEEE Low-Power Computer Vision Challenge to advance the state of the art in low-power computer vision. We describe the competition organizing details including the challenge design, the reference solution, the dataset, the referee system, and the evolution of the solutions from two winning teams. We examine the winning teams’ development patterns and design decisions, focusing on their techniques to balance power consumption and accuracy. We conclude that a successful competition needs a well-designed reference solution and automated referee system, and a solution with modularized components is more likely to win. We hope this paper provides guidelines for future organizers and contestants of computer vision competitions.
X. Hu, Z. Jiao, A. Kocher, Z. Wu, J. Liu, J.C. Davis, G.K. Thiruvathukal, Y.H. Lu. Evolution of Winning Solutions in the 2021 Low-Power Computer Vision Challenge. IEEE Computer, 2023, doi: 10.1109/MC.2023.3250246
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