  The major approaches of transfer learning in computer vision have tried to
adapt the source domain to the target domain one-to-one. However, this scenario
is difficult to apply to real applications such as video surveillance systems.
As those systems have many cameras installed at each location regarded as
source domains, it is difficult to identify the proper source domain. In this
paper, we introduce a new transfer learning scenario that has various source
domains and one target domain, assuming video surveillance system integration.
Also, we propose a novel method for automatically producing a high accuracy
model by fusing models trained at various source domains. In particular, we
show how to apply a gating network to fuse source domains for object detection
tasks, which is a new approach. We demonstrate the effectiveness of our method
through experiments on traffic surveillance datasets.
