  Person re-identification plays an important role in realistic video
surveillance with increasing demand for public safety. In this paper, we
propose a novel framework with rules of updating images for person
re-identification in real-world surveillance system. First, Image Pool is
generated by using mean-shift tracking method to automatically select video
frame fragments of the target person. Second, features extracted from Image
Pool by convolutional network work together to re-rank original ranking list of
the main image and matching results will be generated. In addition, updating
rules are designed for replacing images in Image Pool when a new image
satiating with our updating critical formula in video system. These rules fall
into two categories: if the new image is from the same camera as the previous
updated image, it will replace one of assist images; otherwise, it will replace
the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and
PRID-2011 and our ITSD datasets to validate that our framework outperforms on
rank-1 accuracy and mAP for person re-identification. Furthermore, the update
ability of our framework provides consistently remarkable accuracy rate in
real-world surveillance system.
