Person re-identification by multi-channel feature cascading

Person re-identification is a non-trivial problem due to many challenging factors such as different views or varying illumination. The person re-identification problem is discussed in views of feature cascading and combining in this paper. After a detailed analysis of the disadvantages of the ELF (Ensemble of Localized Features) method, features by cascading multiple channels at proper regions are investigated on the reidentification performance. In the proposed exploring framework, it´s found that the cascading histogram feature with YCbCrHS channels within interior regions can achieve excellent performance. The proposed excellent multi-channel cascading feature (MCCF) is then combined with other features for further improvement. Extensive validation and comparative experiments were conducted on the public gallery VIPeR. And the experimental results show that the proposed feature MCCF and the combined feature can achieve comparable or better performance on person re-identification than other state-of-the- art features.