  Moving objects are frequently seen in daily life and usually appear blurred
in images due to their motion. While general object retrieval is a widely
explored area in computer vision, it primarily focuses on sharp and static
objects, and retrieval of motion-blurred objects in large image collections
remains unexplored. We propose a method for object retrieval in images that are
affected by motion blur. The proposed method learns a robust representation
capable of matching blurred objects to their deblurred versions and vice versa.
To evaluate our approach, we present the first large-scale datasets for blurred
object retrieval, featuring images with objects exhibiting varying degrees of
blur in various poses and scales. We conducted extensive experiments, showing
that our method outperforms state-of-the-art retrieval methods on the new
blur-retrieval datasets, which validates the effectiveness of the proposed
approach. Code, data, and model are available at
https://github.com/Rong-Zou/Retrieval-Robust-to-Object-Motion-Blur.
