Exploit the Unknown Gradually: One-Shot Video-Based Person
Re-Identification by Stepwise Learning
Abstract
We focus on the one-shot learning for video-based
person re-Identification (re-ID). Unlabeled tracklets for
the person re-ID tasks can be easily obtained by preprocessing, such as pedestrian detection and tracking. In
this paper, we propose an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network
(CNN) feature representation via stepwise learning. We first
initialize a CNN model using one labeled tracklet for each
identity. Then we update the CNN model by the following
two steps iteratively: 1. sample a few candidates with most
reliable pseudo labels from unlabeled tracklets; 2. update
the CNN model according to the selected data. Instead
of the static sampling strategy applied in existing works,
we propose a progressive sampling method to increase the
number of the selected pseudo-labeled candidates step by
step. We systematically investigate the way how we should
select pseudo-labeled tracklets into the training set to make
the best use of them. Notably, the rank-1 accuracy of our
method outperforms the state-of-the-art method by 21.46
points (absolute, i.e., 62.67% vs. 41.21%) on the MARS
dataset, and 16.53 points on the DukeMTMC-VideoReID
dataset