relation: https://khub.utp.edu.my/scholars/20037/
title: Curated Dataset Generation for Enhanced Security: ST-PID Dataset
creator: Pitafi, S.
creator: Anwar, T.
creator: Sharif, Z.
creator: Hina
creator: Hassan, Z.
description: The Internet of Things (IoTs) has become ubiquitous in daily life, contributing to environmental sensing and information collection in areas such as smart cities, healthcare systems, intelligent transportation, and smart homes. Additionally, an important application of IoT is Perimeter Intrusion Detection (PID), crucial for monitoring commercial, residential, or industrial locations. While many of the researchers have been focused on intrusion detection systems but PID still lacking a particular dataset for experimenting with machine learning and deep learning algorithms to find out the better false alarm rate (FAR) and probability of detection (POD). Considering these challenges, this paper introduces a curated dataset named ST-PID, captured from various cameras at different locations, recording videos over a three-month period, including day and night monitoring. The dataset is enriched through preprocessing, image augmentation, and labeling, resulting in 8000 images depicting various intrusion activities. YOLO V5 is then applied to successfully detect six classes of intrusion activities for dataset evaluation. © 2024 IEEE.
date: 2024
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Pitafi, S. and Anwar, T. and Sharif, Z. and Hina and Hassan, Z.  (2024) Curated Dataset Generation for Enhanced Security: ST-PID Dataset.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191658268&doi=10.1109%2fKST61284.2024.10499688&partnerID=40&md5=524740bbae3451e1e24f7cbd3a9a6ece
relation: 10.1109/KST61284.2024.10499688
identifier: 10.1109/KST61284.2024.10499688