  Person re-identification is an important task in video surveillance that aims
to associate people across camera views at different locations and time. View
variability is always a challenging problem seriously degrading person
re-identification performance. Most of the existing methods either focus on how
to learn view invariant feature or how to combine view-wise features. In this
paper, we mainly focus on how to learn view-invariant features by getting rid
of view specific information through a view confusion learning mechanism.
Specifically, we propose an end-toend trainable framework, called View
Confusion Feature Learning (VCFL), for person Re-ID across cameras. To the best
of our knowledge, VCFL is originally proposed to learn view-invariant
identity-wise features, and it is a kind of combination of view-generic and
view-specific methods. Classifiers and feature centers are utilized to achieve
view confusion. Furthermore, we extract sift-guided features by using
bag-of-words model to help supervise the training of deep networks and enhance
the view invariance of features. In experiments, our approach is validated on
three benchmark datasets including CUHK01, CUHK03, and MARKET1501, which show
the superiority of the proposed method over several state-of-the-art approaches
