DeepThermal Outdoor: A first-person thermal imaging dataset
360 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE6.2022.92-104Keywords:
Artificial intelligence; Thermal image; Human detection.Abstract
Recently, thermal imaging modules equipped for infantry soldiers have been a trend to improve the combat ability of soldiers. Soldiers have to perform many different tasks at the same time, so it is necessary to equip them with the tools of automatic target detection, especially human objects detection, in practice. Hence, there is a need to intelligently optimize the effectiveness of thermal imaging equipment. New artificial intelligence and deep learning(DL) approaches are applicable methods that show superior accuracy compared to previous methods. However, state-of-the-art DL methods depend on the generality and diversity of the training data set. To address this issue, our paper presents the DeepThermal Outdoor thermal imaging data set, which is collected from equipment mounted on the body of infantry at various terrain locations. The labeled dataset focuses on human objects with different locomotion postures, and it contains 10,190 images and 22,464 labeled human-objects. Finally, the experiment is conducted with several DL methods using the proposed dataset, and the results show its contribution to the improvement of the performance of DL methods to detect humans on thermal images as well as to evaluate the practical applicability of a DL.
References
[1]. ARC4: Augmented Reality Command Control Communicate and Coordinate. https://www.ara.com/arc4/
[2]. MOHOC production. https://www.mohoc.com/product/
[3]. A. Toet et al. Tno image fusion dataset. https://doi.org/10.6084/m9.figshare.1008029.v1.
[4]. J. W. Davis and V. Sharma. “Otcbvs benchmark dataset collection”. http://vcipl-okstate.org/pbvs/bench/, 2007.
[5]. S. Hwang, J. Park, N. Kim, Y. Choi, and I. S. Kweon. “Multispectral pedestrian detection: Benchmark dataset and baselines” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015). DOI: https://doi.org/10.1109/CVPR.2015.7298706
[6]. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation” in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, (2015). DOI: https://doi.org/10.1007/978-3-319-24574-4_28
[7]. M. A. Keck, J.W. Davis, “A two-stage template approach to person detection in thermal imagery”. In: Proc. Wkshp. Applications of Comp. Vision, (2005).
[8]. M. Arens, K. Jungling, “Feature based person detection beyond the visible spectrum” in IEEE CVPR Workshops, (2009). DOI: https://doi.org/10.1109/CVPRW.2009.5204085
[9]. T. Tuytelaars, H. Bay, L. V. Gool, “Surf: Speeded up robust features” in: Proc. 9th European Conference on Computer Vision, Graz, Austria, (2006).
[10]. W. Wang, J. Zhang, C. Shen, “Improved human detection and classification in thermal images” in: IEEE 17th International Conference on Image Processing, (2010). DOI: https://doi.org/10.1109/ICIP.2010.5649946
[11]. B. Qi, V. John, Z. Liu, S. Mita, “Use of sparse representation for pedestrian detection in thermal images” in: CVPR workshop, IEEE, (2014).
[12]. Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016). DOI: https://doi.org/10.1109/CVPR.2016.91
[13]. Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks.", Advances in neural information processing systems 28 (2015).
[14]. Liu, Wei, et al. "SSD: Single shot multibox detector.", in European conference on computer vision. Springer, Cham, (2016). DOI: https://doi.org/10.1007/978-3-319-46448-0_2
[15]. Jia, Xinyu and Zhu, Chuang and Li, Minzhen and Tang, Wenqi and Zhou, Wenli, “LLVIP: A Visible-infrared Paired Dataset for Low-light Vision”, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021). DOI: https://doi.org/10.1109/ICCVW54120.2021.00389
[16]. K. R. Akshatha, et al. "Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms." in Electronics, (2022). DOI: https://doi.org/10.3390/electronics11071151
[17]. N. U. Huda, B. D. Hansen, R. Gade, T. B. Moeslund, “The effect of a diverse dataset for transfer learning in thermal person detection”, in Sensors, (2020). DOI: https://doi.org/10.3390/s20071982
[18]. Devaguptapu, Chaitanya, et al., "Borrow from anywhere: Pseudo multi-modal object detection in thermal imagery.", in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2019). DOI: https://doi.org/10.1109/CVPRW.2019.00135
