  This paper proposes a pedestrian detection and re-identification (re-id)
integration net (I-Net) in an end-to-end learning framework. The I-Net is used
in real-world video surveillance scenarios, where the target person needs to be
searched in the whole scene videos, while the annotations of pedestrian
bounding boxes are unavailable. By comparing to the OIM which is a work for
joint detection and re-id, we have three distinct contributions. First, we
introduce a Siamese architecture of I-Net instead of 1 stream, such that a
verification task can be implemented. Second, we propose a novel on-line
pairing loss (OLP) and hard example priority softmax loss (HEP), such that only
the hard negatives are posed much attention in loss computation. Third, an
on-line dictionary for negative samples storage is designed in I-Net without
recording the positive samples. We show our result on person search datasets,
the gap between detection and re-identification is narrowed. The superior
performance can be achieved.
