A codebook of brightness transfer functions for improved target re-identification across non-overlapping camera views

Target re-identification across non-overlapping camera views is a challenging task due to variations in target appearance, illumination, viewpoint and intrinsic parameters of cameras. Brightness transfer function (BTF) was introduced for intercamera color calibration, and to improve the performance of target re-identification methods. There have been several works based on BTFs, more specifically using weighted BTFs (WBTF), cumulative BTF (CBTF) and mean BTF (MBTF). In this paper, we present a novel method to model the appearance variation across different camera views. We propose building a codebook of BTFs composed of the most representative BTFs for a camera pair. We also propose an ordering and trimming criteria to avoid using all possible combinations of codewords for different color channels. In addition, to obtain a better appearance model, we present a different way to segment a target from the background. Evaluations on VIPeR, CUHK01 and CAVIAR4REID datasets show that the proposed method outperforms other approaches focusing on BTFs, including WBTF, CBTF and MBTF. As proven by the results, the proposed method provides an improved brightness transfer across different camera views, and any target ReID approach incorporating color/brightness histograms can benefit from it.