Person Re-Identification by Support Vector Ranking

Solving the person re-identification problem involves matching observations of individuals across disjoint camera views. The problem becomes particularly hard in a busy public scene as the number of possible matches is very high. This is further compounded by significant appearance changes due to varying lighting conditions, viewing angles and body poses across camera views. To address this problem, existing approaches focus on extracting or learning discriminative features followed by template matching using a distance measure. The novelty of this work is that we reformulate the person reidentification problem as a ranking problem and learn a subspace where the potential true match is given highest ranking rather than any direct distance measure. By doing so, we convert the person re-identification problem from an absolute scoring problem to a relative ranking problem. We further develop an novel Ensemble RankSVM to overcome the scalability limitation problem suffered by existing SVM...

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Bryan Prosser, Wei-Shi Zheng, Shaogang Gong, Tao Xiang
• Keywords: BMVC 2010, Camera Views, Computer Vision, Person Re-identification Problem, Ranking Problem