Package theft detection has been a challenging task mainly due to lack of training data and a wide variety of package theft cases in reality. In this paper, we propose a new Global and Local Fusion Package Theft Detection Embedding (GLF-PTDE) framework to generate package theft scores for each segment within a video to fulfill the real-world requirements on package theft detection. Moreover, we construct a novel Package Theft Detection dataset to facilitate the research on this task. Our method achieves 80% AUC performance on the newly proposed dataset, showing the effectiveness of the proposed GLF-PTDE framework and its robustness in different real scenes for package theft detection.
翻译:软件包盗窃发现是一项具有挑战性的任务,主要原因是缺乏培训数据,而且实际中有各种各样的软件包盗窃案件。在本文件中,我们提议建立一个新的全球和地方软件包组合盗窃探测嵌入式(GLF-PTDE)框架,在视频中为每个部分生成软件包盗窃分数,以满足对软件包盗窃探测的现实世界要求。此外,我们还建立了一个新软件包盗窃探测数据集,以便利对这项工作的研究。我们的方法在新提议的数据集上实现了80%的软件包探测功能,显示了拟议软件包盗窃探测框架的有效性及其在不同真实场景的可靠性,以发现软件包盗窃。