  Camera traps enable the automatic collection of large quantities of image
data. Ecologists use camera traps to monitor animal populations all over the
world. In order to estimate the abundance of a species from camera trap data,
ecologists need to know not just which species were seen, but also how many
individuals of each species were seen. Object detection techniques can be used
to find the number of individuals in each image. However, since camera traps
collect images in motion-triggered bursts, simply adding up the number of
detections over all frames is likely to lead to an incorrect estimate.
Overcoming these obstacles may require incorporating spatio-temporal reasoning
or individual re-identification in addition to traditional species detection
and classification.
  We have prepared a challenge where the training data and test data are from
different cameras spread across the globe. The set of species seen in each
camera overlap, but are not identical. The challenge is to classify species and
count individual animals across sequences in the test cameras.
