PDIWS: Thermal Imaging Dataset for Person Detection in Intrusion Warning Systems
This provides a dataset for researchers working on thermal imaging-based intrusion detection, but it is incremental as it focuses on synthetic data generation.
The paper tackles the problem of person detection in intrusion warning systems by presenting a synthetic thermal imaging dataset (PDIWS) with 2000 training and 500 test images, achieving up to 95.5% mAP at IoU 0.5 using advanced object detection algorithms.
In this paper, we present a synthetic thermal imaging dataset for Person Detection in Intrusion Warning Systems (PDIWS). The dataset consists of a training set with 2000 images and a test set with 500 images. Each image is synthesized by compounding a subject (intruder) with a background using the modified Poisson image editing method. There are a total of 50 different backgrounds and nearly 1000 subjects divided into five classes according to five human poses: creeping, crawling, stooping, climbing and other. The presence of the intruder will be confirmed if the first four poses are detected. Advanced object detection algorithms have been implemented with this dataset and give relatively satisfactory results, with the highest mAP values of 95.5% and 90.9% for IoU of 0.5 and 0.75 respectively. The dataset is freely published online for research purposes at https://github.com/thuan-researcher/Intruder-Thermal-Dataset.