POP: Person Re-Identification Post-Rank Optimisation

Owing to visual ambiguities and disparities, person re-identification methods inevitably produce suboptimal 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 OPtimisation (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 quantitative evaluations we demonstrate that the...

• Type: Conference
• Year: 2013
• Where: ICCV
• Authors: C. Liu, C. C. Loy, S. Gong, G. Wang
• Keywords: Feature Selection, Person Re-identification, Random Forest, Ranking, Visual Surveillance