  Digital technologies have made it possible to deploy visual sensor nodes
capable of detecting motion events in the coverage area cost-effectively.
However, background subtraction, as a widely used approach, remains an
intractable task due to its inability to achieve competitive accuracy and
reduced computation cost simultaneously. In this paper, an effective background
subtraction approach, namely NeSe, for tiny energy-harvested sensors is
proposed leveraging non-volatile memory (NVM). Using the developed
software/hardware method, the accuracy and efficiency of event detection can be
adjusted at runtime by changing the precision depending on the application's
needs. Due to the near-sensor implementation of background subtraction and NVM
usage, the proposed design reduces the data movement overhead while ensuring
intermittent resiliency. The background is stored for a specific time interval
within NVMs and compared with the next frame. If the power is cut, the
background remains unchanged and is updated after the interval passes. Once the
moving object is detected, the device switches to the high-powered sensor mode
to capture the image.
