  Is recurrent network really necessary for learning a good visual
representation for video based person re-identification (VPRe-id)? In this
paper, we first show that the common practice of employing recurrent neural
networks (RNNs) to aggregate temporal spatial features may not be optimal.
Specifically, with a diagnostic analysis, we show that the recurrent structure
may not be effective to learn temporal dependencies than what we expected and
implicitly yields an orderless representation. Based on this observation, we
then present a simple yet surprisingly powerful approach for VPRe-id, where we
treat VPRe-id as an efficient orderless ensemble of image based person
re-identification problem. More specifically, we divide videos into individual
images and re-identify person with ensemble of image based rankers. Under the
i.i.d. assumption, we provide an error bound that sheds light upon how could we
improve VPRe-id. Our work also presents a promising way to bridge the gap
between video and image based person re-identification. Comprehensive
experimental evaluations demonstrate that the proposed solution achieves
state-of-the-art performances on multiple widely used datasets (iLIDS-VID, PRID
2011, and MARS).
