Ensemble of Both Texture and Color Features for Reliable Person Re-Identification

Here a set of non trained person re-identification approaches is proposed for obtaining an high performance system, the proposed system has been tested on four datasets (CAVIAR4REID, IAS, BIWI and VIPeR). To reduce the risk of overfitting, for all the methods, the parameters have been kept constant across all datasets. The ensemble proposed in this work is based on different enhancement techniques, colorimetric spaces and state-of-the-art approaches. For the datasets where the depth map is available also a method based on skeleton detection, extracted from the depth map, belongs to the ensemble. In our opinion, the proposed ensemble can be considered a general-purpose person re-identification system since all the parameters are not optimized separately in each dataset but are fixed. The source code used for the approaches tested in this paper will be available at (https://www.dei.unipd.it/node/2357 + Pattern Recognition and Ensemble Classifiers).