Package Theft

Package theft is a growing problem prompting research into automated detection systems using video surveillance. Current research focuses on developing robust and generalizable deep learning models, often employing techniques like transfer learning and model fusion, to identify theft events from diverse video sources with minimal retraining. These advancements, along with the creation of specialized datasets, aim to improve the accuracy and efficiency of package theft detection, offering potential for enhanced security and loss prevention in various settings.

Papers

July 17, 2024

A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod, Laith Alzubaidi, Ahmed Ali Saihood, YuanTong Gu
Neural Network Deep Learning Anomaly Detection Surveillance Video Violence Detection Generalizable Feature Package Theft

August 11, 2023

Towards Packaging Unit Detection for Automated Palletizing Tasks
Markus Völk, Kilian Kleeberger, Werner Kraus, Richard Bormann
Data Detection Industrial Robot Packaging Unit Package Theft

August 12, 2022

Feature-Based Time-Series Analysis in R using the theft Package
Trent Henderson, Ben D. Fulcher
Time Series Classification Time Series Datasets Time Series Feature Package Theft

May 24, 2022

Package Theft Detection from Smart Home Security Cameras
Hung-Min Hsu, Xinyu Yuan, Baohua Zhu, Zhongwei Cheng, Lin Chen
Package Theft

Package Theft

Papers

A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos

Towards Packaging Unit Detection for Automated Palletizing Tasks

Feature-Based Time-Series Analysis in R using the theft Package

Package Theft Detection from Smart Home Security Cameras