Dual Channel Gradient feature for person re-identification

Recently, several effective features were proposed for person re-identification, such as Weight Histograms of Overlapping Stripes (WHOS) and Local Maximal Occurrence (LOMO), but it still need to explore new effective feature to improve the precision for person re-identification. So, in this paper, we proposed a new Dual Channel Gradient feature, which can be fused with WHOS and LOMO by directly concatenating the normalized histograms. Experimental results showed that the fused feature can achieve the state-of-the-art accuracy, on four public datasets with two different metric learning methods.