Abstract
Object detection is a computer vision technique that provides the most fundamental information for computer vision applications: “What objects are where?”, which has achieved significant progress thanks to the great success of deep learning in the past decade. With this kind of identification and localization, object detection is able to identify objects and track their precise locations in a scene. However, information beyond what and where is fairly desirable for more advanced applications, such as scene understanding, autonomous driving, and service robots, in which knowing the behavior or state or attribute of the objects is important. In this paper, we concern about the occlusion state of the target as well as its identification and localization, which is crucial for a service robot to keep track of a target or to grasp an object, for instance. We present a Dataset for Detecting Occlusion State of Objects (DDOSO). This benchmark aims to encourage research in developing novel and accurate methods for this challenging task. DDOSO contains 10 categories of bounding-box annotations collected from 6959 images. Based on this well-annotated dataset, we build baselines over two state-of-the-art algorithms. By releasing DDOSO, we expect to facilitate future researches on detecting the occlusion state of objects and draw more attention to object detection beyond what and where.
Thanks to the support by Guangxi Science and Technology Base and Talent Special Project (No. Guike AD22035127).
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Qin, L., Zhou, H., Wang, Z., Deng, J., Liao, Y., Li, S. (2022). Detection Beyond What and Where: A Benchmark for Detecting Occlusion State. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_38
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