  Video-based person re-identification has received increasing attention
recently, as it plays an important role within surveillance video analysis.
Video-based Re-ID is an expansion of earlier image-based re-identification
methods by learning features from a video via multiple image frames for each
person. Most contemporary video Re-ID methods utilise complex CNNbased network
architectures using 3D convolution or multibranch networks to extract
spatial-temporal video features. By contrast, in this paper, we illustrate
superior performance from a simple single stream 2D convolution network
leveraging the ResNet50-IBN architecture to extract frame-level features
followed by temporal attention for clip level features. These clip level
features can be generalised to extract video level features by averaging
without any significant additional cost. Our approach uses best video Re-ID
practice and transfer learning between datasets to outperform existing
state-of-the-art approaches on the MARS, PRID2011 and iLIDS-VID datasets with
89:62%, 97:75%, 97:33% rank-1 accuracy respectively and with 84:61% mAP for
MARS, without reliance on complex and memory intensive 3D convolutions or
multi-stream networks architectures as found in other contemporary work.
Conversely, our work shows that global features extracted by the 2D convolution
network are a sufficient representation for robust state of the art video
Re-ID.
