Catch Me if You Can：A Novel Task for Detection of Covert Geo-Locations（CGL）
Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL)
摘要：
计算机视觉领域中的大多数视觉场景理解任务涉及对场景中存在的对象的识别。像隐藏处、转弯和场景的其他模糊区域这样的图像区域也包含用于特定监视任务的关键信息。在本文中提出的任务涉及到设计一个智能视觉辅助识别这样的位置在图像中，这有可能造成迫在眉睫的威胁，从对手或出现的目标区需要进一步调查。隐藏在遮挡对象后面的隐蔽位置（CGL）是隐藏的3D位置，从视点（相机）无法检测到。因此，这涉及在遮挡对象的外边界的投影周围描绘特定的图像区域，作为在潜在隐藏处周围要访问的地方。CGL检测发现在军事反叛乱行动中的应用，监视与探索机器人的路径规划。给定RGB图像，目标是识别2D场景中的所有CGL。这些区域的识别将需要关于模糊物品（支柱、家具）的3D边界、它们相对于场景的相邻区域的空间位置的知识。我们提出这是一个新的任务，称为隐蔽地理位置（CGL）检测。将图像的任何区域分类为CGL（作为隐藏处的遮挡对象的边界子段）需要检查遮挡对象的边界与其邻域和周围环境之间的3D关系。我们的方法成功地提取相关的深度特征，从一个单一的RGB图像和定量产生显着改善现有的目标检测和分割模型适应和训练CGL检测。我们还介绍了一种新的手工注释的CGL检测数据集，包含1.5K真实世界的图像进行实验。
英文摘要：
Most visual scene understanding tasks in the field of computer vision involve identification of the objects present in the scene. Image regions like hideouts, turns, & other obscured regions of the scene also contain crucial information, for specific surveillance tasks. Task proposed in this paper involves the design of an intelligent visual aid for identification of such locations in an image, which has either the potential to create an imminent threat from an adversary or appear as the target zones needing further investigation. Covert places (CGL) for hiding behind an occluding object are concealed 3D locations, not detectable from the viewpoint (camera). Hence this involves delineating specific image regions around the projections of outer boundary of the occluding objects, as places to be accessed around the potential hideouts. CGL detection finds applications in military counter-insurgency operations, surveillance with path planning for an exploratory robot. Given an RGB image, the goal is to identify all CGLs in the 2D scene. Identification of such regions would require knowledge about the 3D boundaries of obscuring items (pillars, furniture), their spatial location with respect to the neighboring regions of the scene. We propose this as a novel task, termed Covert Geo-Location (CGL) Detection. Classification of any region of an image as a CGL (as boundary sub-segments of an occluding object that conceals the hideout) requires examining the 3D relation between boundaries of occluding objects and their neighborhoods & surroundings. Our method successfully extracts relevant depth features from a single RGB image and quantitatively yields significant improvement over existing object detection and segmentation models adapted and trained for CGL detection. We also introduce a novel hand-annotated CGL detection dataset containing 1.5K real-world images for experimentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
