POP: Person re-identification post-rank optimisation

Owing to visual ambiguities and disparities, person re-identification methods inevitably produce sub optimal rank-list, which still requires exhaustive human eyeballing to identify the correct target from hundreds of different likely-candidates. Existing re-identification studies focus on improving the ranking performance, but rarely look into the critical problem of optimising the time-consuming and error-prone post-rank visual search at the user end. In this study, we present a novel one-shot Post-rank Optimization (POP) method, which allows a user to quickly refine their search by either 'one-shot' or a couple of sparse negative selections during a re-identification process. We conduct systematic behavioural studies to understand user's searching behaviour and show that the proposed method allows correct re-identification to converge 2.6 times faster than the conventional exhaustive search. Importantly, through extensive evaluations we demonstrate that the method is capable of achieving significant improvement over the state-of-the-art distance metric learning based ranking models, even with just 'one shot' feedback optimisation, by as much as over 30% performance improvement for rank 1 re-identification on the VIPeR and i-LIDS datasets.