  The automatic characterization of pedestrians in surveillance footage is a
tough challenge, particularly when the data is extremely diverse with cluttered
backgrounds, and subjects are captured from varying distances, under multiple
poses, with partial occlusion. Having observed that the state-of-the-art
performance is still unsatisfactory, this paper provides a novel solution to
the problem, with two-fold contributions: 1) considering the strong semantic
correlation between the different full-body attributes, we propose a multi-task
deep model that uses an element-wise multiplication layer to extract more
comprehensive feature representations. In practice, this layer serves as a
filter to remove irrelevant background features, and is particularly important
to handle complex, cluttered data; and 2) we introduce a weighted-sum term to
the loss function that not only relativizes the contribution of each task (kind
of attributed) but also is crucial for performance improvement in
multiple-attribute inference settings. Our experiments were performed on two
well-known datasets (RAP and PETA) and point for the superiority of the
proposed method with respect to the state-of-the-art. The code is available at
https://github.com/Ehsan-Yaghoubi/MAN-PAR-.
