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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >PANORAMIC IMAGES, 2D FEATURE-BASED AND CHANGE DETECTION METHODS FOR THE DOCUMENTATION OF CONTAMINATED CRIME SCENES
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PANORAMIC IMAGES, 2D FEATURE-BASED AND CHANGE DETECTION METHODS FOR THE DOCUMENTATION OF CONTAMINATED CRIME SCENES

机译：用于污染犯罪现场记录的全景图像，基于2D特征的检测方法和变化检测方法

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This paper aims to propose and validate a methodology which can support forensic technicians while documenting a crime scene, after a contamination event (either accidental or deliberate) has changed its original appearance. Indeed, investigators need fast and automated tools to detect changes that occurred at a scene over time, and solutions to this problem are still an open issue. The contribution of the paper for addressing this need is twofold. First, a new methodology is introduced, that exploits panoramic images acquired with the Ricoh Theta SC camera, and 2D feature-based methods. The core idea is that SIFT features inherently contain scene information and, thanks to their good stability and invariance, can be exploited to detect possible changes that occurred at a scene surveyed with multi-temporal images. Second, in order to evaluate the performance of the proposed methodology, a reference approach is applied, based on state-of-the-art change detection algorithms (MAF/MAD), originally developed for remote sensing applications. Both methods are tested by simulating a typical crime scene, and a contamination event at the Crime Scene House (UK).
机译：本文旨在提出并验证一种方法，该方法可以在污染事件（意外或故意的）改变其原始外观后，在支持犯罪现场记录时为法医提供支持。确实，研究人员需要快速，自动化的工具来检测现场随时间变化的情况，而解决该问题的方法仍然是一个未解决的问题。本文为解决这一需求做出了双重贡献。首先，引入了一种新方法，该方法利用了由理光Theta SC相机采集的全景图像以及基于2D特征的方法。核心思想是SIFT特征固有地包含场景信息，并且由于其良好的稳定性和不变性，可以利用它来检测在用多时间图像进行调查的场景中可能发生的变化。其次，为了评估所提出方法的性能，基于最初为遥感应用开发的最新变化检测算法（MAF / MAD），应用了一种参考方法。两种方法均通过模拟典型的犯罪现场和英国犯罪现场所的污染事件进行测试。

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