A multi-image Joint Re-ranking framework with updateable Image Pool for person re-identification

Abstract

Real-world video surveillance has increasing demand for person re-identification. Existing multi-shot works usually aggregate single sample features by computing the average features or using time series model. The Multi-image Joint Re-ranking framework with updateable Image Pool that we are proposing will give a different approach. First, we defined the term ‘Image Pool’ to store image samples for each pedestrian. Next, the updating rules of Image Pool has been defined in order to optimize the representativeness of it. Second, we compute initial ranking lists of every sample in Image Pool, and propose the ‘Multiple-image Joint Re-ranking’ algorithm to aggregate initial ranking lists. We calculate the rank score of partial elements of initial ranking lists. In the end, we get final ranking list by ascending the order of the rank scores. We validated our re-ranking results on Market-1501, iLIDS-VID, PRID-2011 and our ITSD datasets, and the results outperform other methods.