Recognizing the pedestrian attributes in surveillance
scenes is an inherently challenging task, especially for
the pedestrian images with large pose variations, complex backgrounds,
and various camera viewing angles. To select important
and discriminative regions or pixels against the variations, three
attention mechanisms are proposed, including parsing attention,
label attention, and spatial attention. Those attentions aim at
accessing effective information by considering problems from
different perspectives. To be specific, the parsing attention
extracts discriminative features by learning not only where to
turn attention to but also how to aggregate features from different
semantic regions of human bodies, e.g., head and upper body. The
label attention aims at targetedly collecting the discriminative
features for each attribute. Different from the parsing and label
attention mechanisms, the spatial attention considers the problem
from a global perspective, aiming at selecting several important
and discriminative image regions or pixels for all attributes.
Then, we propose a joint learning framework formulated in
a multi-task-like way with these three attention mechanisms
learned concurrently to extract complementary and correlated
features. This joint learning framework is named Joint Learning
of Parsing attention, Label attention, and Spatial attention for
Pedestrian Attributes Analysis (JLPLS-PAA, for short). Extensive
comparative evaluations conducted on multiple large-scale
benchmarks, including PA-100K, RAP, PETA, Market-1501, and
Duke attribute datasets, further demonstrate the effectiveness of the proposed JLPLS-PAA framework for pedestrian attribute
analysis.