  Deep learning methods have started to dominate the research progress of
video-based person re-identification (re-id). However, existing methods mostly
consider supervised learning, which requires exhaustive manual efforts for
labelling cross-view pairwise data. Therefore, they severely lack scalability
and practicality in real-world video surveillance applications. In this work,
to address the video person re-id task, we formulate a novel Deep Association
Learning (DAL) scheme, the first end-to-end deep learning method using none of
the identity labels in model initialisation and training. DAL learns a deep
re-id matching model by jointly optimising two margin-based association losses
in an end-to-end manner, which effectively constrains the association of each
frame to the best-matched intra-camera representation and cross-camera
representation. Existing standard CNNs can be readily employed within our DAL
scheme. Experiment results demonstrate that our proposed DAL significantly
outperforms current state-of-the-art unsupervised video person re-id methods on
three benchmarks: PRID 2011, iLIDS-VID and MARS.
