  The event camera is a novel bio-inspired vision sensor. When the brightness
change exceeds the preset threshold, the sensor generates events
asynchronously. The number of valid events directly affects the performance of
event-based tasks, such as reconstruction, detection, and recognition. However,
when in low-brightness or slow-moving scenes, events are often sparse and
accompanied by noise, which poses challenges for event-based tasks. To solve
these challenges, we propose an event temporal up-sampling algorithm1 to
generate more effective and reliable events. The main idea of our algorithm is
to generate up-sampling events on the event motion trajectory. First, we
estimate the event motion trajectory by contrast maximization algorithm and
then up-sampling the events by temporal point processes. Experimental results
show that up-sampling events can provide more effective information and improve
the performance of downstream tasks, such as improving the quality of
reconstructed images and increasing the accuracy of object detection.
