Learning to Recognize Pedestrian Attribute
This work addresses a challenge in visual surveillance for security applications, but it is incremental as it builds on existing methods with a new dataset.
The paper tackles the problem of recognizing pedestrian attributes from far-view images by proposing a method that uses context from neighboring images, achieving improved inference over conventional SVM-based approaches.
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.