  With its large field of view, GWAC can record hundreds of meteors every day.
These meteors are valuable treasures for some meteor research groups. It is
therefore very important to accurately find all of these meteors. To address
the challenge of precisely distinguishing meteors from other elongated objects
in a GWAC-like sky survey system, we design and implement a meteor candidate
recognition algorithm, including the recognizing and morphology analysis of the
light curves of the meteor candidates. Although the algorithm may filter out
some real meteors, it can provide a sample of meteor with high confidence.
After processing the images of Mini-GWAC taken in two months, we detect 109,000
elongated objects in which more than 90 percent of objects are not meteor.
Among the elongated objects, about 5.9% objects are identified as meteors with
high confidence, after the filters based upon an existence in a single frame, a
single peak in the light curves, and a slow variation of the light curves.
