  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.
