  Video based person re-identification plays a central role in realistic
security and video surveillance. In this paper we propose a novel Accumulative
Motion Context (AMOC) network for addressing this important problem, which
effectively exploits the long-range motion context for robustly identifying the
same person under challenging conditions. Given a video sequence of the same or
different persons, the proposed AMOC network jointly learns appearance
representation and motion context from a collection of adjacent frames using a
two-stream convolutional architecture. Then AMOC accumulates clues from motion
context by recurrent aggregation, allowing effective information flow among
adjacent frames and capturing dynamic gist of the persons. The architecture of
AMOC is end-to-end trainable and thus motion context can be adapted to
complement appearance clues under unfavorable conditions (e.g. occlusions).
Extensive experiments are conduced on three public benchmark datasets, i.e.,
the iLIDS-VID, PRID-2011 and MARS datasets, to investigate the performance of
AMOC. The experimental results demonstrate that the proposed AMOC network
outperforms state-of-the-arts for video-based re-identification significantly
and confirm the advantage of exploiting long-range motion context for video
based person re-identification, validating our motivation evidently.
