Hung-Min Hsu, Xinyu Yuan, Baohua Zhu, Zhongwei Cheng, Lin Chen
{"title":"Package Theft Detection from Smart Home Security Cameras","authors":"Hung-Min Hsu, Xinyu Yuan, Baohua Zhu, Zhongwei Cheng, Lin Chen","doi":"10.1109/ICMEW56448.2022.9859522","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEW56448.2022.9859522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.