  Given a video or an image of a person acquired from a camera, person
re-identification is the process of retrieving all instances of the same person
from videos or images taken from a different camera with non-overlapping view.
This task has applications in various fields, such as surveillance, forensics,
robotics, multimedia. In this paper, we present a novel framework, named
Saliency-Semantic Parsing Re-Identification (SSP-ReID), for taking advantage of
the capabilities of both clues: saliency and semantic parsing maps, to guide a
backbone convolutional neural network (CNN) to learn complementary
representations that improves the results over the original backbones. The
insight of fusing multiple clues is based on specific scenarios in which one
response is better than another, thus favoring the combination of them to
increase performance. Due to its definition, our framework can be easily
applied to a wide variety of networks and, in contrast to other competitive
methods, our training process follows simple and standard protocols. We present
extensive evaluation of our approach through five backbones and three
benchmarks. Experimental results demonstrate the effectiveness of our person
re-identification framework. In addition, we combine our framework with
re-ranking techniques to achieve state-of-the-art results on three benchmarks.
