  Learning to recognize pedestrian attributes at far distance is a challenging
problem in visual surveillance since face and body close-shots are hardly
available; instead, only far-view image frames of pedestrian are given. In this
study, we present an alternative approach that exploits the context of
neighboring pedestrian images for improved attribute inference compared to the
conventional SVM-based method. In addition, we conduct extensive experiments to
evaluate the informativeness of background and foreground features for
attribute recognition. Experiments are based on our newly released pedestrian
attribute dataset, which is by far the largest and most diverse of its kind.
