Feature Fusion and Discriminative Null Space for Person Re-identification

The person re-identification technology is vulnerable to the light, posture and view perspective, thus appearance feature is changed seriously, it is difficult to extract invariant features, which will lead to the problem of the low recognition rate. In this paper, a method of feature fusion and discriminative null space for person reidentification is proposed. Firstly, the stripe feature is extracted by HSV, LAB, RGB and YCrCb four color characteristics and Gabor filter, and GOG descriptor is used to extracted block feature, then those features are concatenated into a whole feature vector, which is projected into discriminative null space to reduce their feature dimension. Finally, Euclidean distance is applied to compute personal distance to achieve re-identify person. Experimental results show that, rank1 recognition rate of the proposed method could achieve 52.7%, 72.2% and 59.7% on VIPeR、Prids_450s and CUHK01 database respectively, which shows that the proposed method can sufficiently describe personal picture feature, and has a strong robustness for environment which can effectively improve the recognition rate.