  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.
