Retrieval Robust to Object Motion Blur

• URL: http://arxiv.org/abs/2404.18025v2
• Date: Wed, 17 Jul 2024 21:57:16 GMT
• Title: Retrieval Robust to Object Motion Blur
• Authors: Rong Zou, Marc Pollefeys, Denys Rozumnyi,
• Abstract summary: We propose a method for object retrieval in images that are affected by motion blur. We present the first large-scale datasets for blurred object retrieval. Our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets.
• Score: 54.34823913494456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Code, data, and model are available at https://github.com/Rong-Zou/Retrieval-Robust-to-Object-Motion-Blur.

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