  Video-based person reID is an important task, which has received much
attention in recent years due to the increasing demand in surveillance and
camera networks. A typical video-based person reID system consists of three
parts: an image-level feature extractor (e.g. CNN), a temporal modeling method
to aggregate temporal features and a loss function. Although many methods on
temporal modeling have been proposed, it is hard to directly compare these
methods, because the choice of feature extractor and loss function also have a
large impact on the final performance. We comprehensively study and compare
four different temporal modeling methods (temporal pooling, temporal attention,
RNN and 3D convnets) for video-based person reID. We also propose a new
attention generation network which adopts temporal convolution to extract
temporal information among frames. The evaluation is done on the MARS dataset,
and our methods outperform state-of-the-art methods by a large margin. Our
source codes are released at https://github.com/jiyanggao/Video-Person-ReID.
