  This paper addresses the person re-identification (PReID) problem by
combining global and local information at multiple feature resolutions with
different loss functions. Many previous studies address this problem using
either part-based features or global features. In case of part-based
representation, the spatial correlation between these parts is not considered,
while global-based representation are not sensitive to spatial variations. This
paper presents a part-based model with a multi-resolution network that uses
different level of features. The output of the last two conv blocks is then
partitioned horizontally and processed in pairs with overlapping stripes to
cover the important information that might lie between parts. We use different
loss functions to combine local and global information for classification.
Experimental results on a benchmark dataset demonstrate that the presented
method outperforms the state-of-the-art methods.
