Abstract
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.
Abstract (translated)
移动物体在现实生活中经常可见，通常由于其运动而在图像中显得模糊。虽然计算机视觉中广泛研究的对象检索领域主要关注清晰和静态的对象，但在大型图像集合中检索运动模糊的物体仍然是一个未探索的问题。我们提出了一种在受运动模糊影响的图像中进行对象检索的方法。所提出的方法能够学习到一个健壮的表示，能够将模糊的物体与它们的去模糊版本进行匹配，反之亦然。为了评估我们的方法，我们提出了第一个大型的模糊物体检索数据集，其中包括在各种姿势和尺度下表现出不同程度的模糊的图像。我们进行了广泛的实验，结果表明，与最先进的检索方法相比，我们的方法在新模糊检索数据集上表现出优异的性能，这验证了所提出方法的的有效性。
URL
https://arxiv.org/abs/2404.18025
