A feature fusion strategy for person re-identification
Mu Gao, Haizhou Ai, Bo Bob Bai · 2016
The problem of person re-identification, identifying the same person appeared in different camera views, is an important and challenging task in computer vision that has high potential application in areas like visual surveillance. In this paper we introduce a new feature fusion strategy for person reidentification that combines low-level Weighted Histograms of Overlapping Stripes (WHOS) features with mid-level color name descriptors and we adopt KISSME algorithm for person matching. Experiments on several public person reidentification datasets (VIPeR, i-LIDS and CAVIAR4REID) demonstrate that our approach achieves much better results compared with other state-of-the-art approaches.