  Recognising semantic pedestrian attributes in surveillance images is a
challenging task for computer vision, particularly when the imaging quality is
poor with complex background clutter and uncontrolled viewing conditions, and
the number of labelled training data is small. In this work, we formulate a
Joint Recurrent Learning (JRL) model for exploring attribute context and
correlation in order to improve attribute recognition given small sized
training data with poor quality images. The JRL model learns jointly pedestrian
attribute correlations in a pedestrian image and in particular their sequential
ordering dependencies (latent high-order correlation) in an end-to-end
encoder/decoder recurrent network. We demonstrate the performance advantage and
robustness of the JRL model over a wide range of state-of-the-art deep models
for pedestrian attribute recognition, multi-label image classification, and
multi-person image annotation on two largest pedestrian attribute benchmarks
PETA and RAP.
