AN EVALUATION OF DESIGN CHOICES FOR PEDESTRIAN ATTRIBUTE RECOGNITION IN VIDEO

Person attribute recognition in surveillance data is a challenging task. Attributes are often visible in very localized regions and recognition thus suffers from poor image quality, changing lighting conditions, viewing angles, and occlusions. Previous research has focused predominantly on recognition based on single images. In this work, we investigate the applicability of several recent methods to include temporal information into the recognition process. We identify the most promising building blocks and thus create a strong baseline model, which achieves state of the art attribute recognition accuracy in videos and provides a basis for future research. Finally, we show that the resulting attributes can serve as a basis for description-based person retrieval.