Thermal Imaging Dataset for Person Detection

Krišto, Mate; Ivašić-Kos, Marina

doi:10.23919/mipro.2019.8757208

Cited by 34 publications

(32 citation statements)

References 19 publications

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“…All scenarios in the dataset are recorded in the night and during the winter period. Weather conditions in the recordings vary from clear weather, fog, and heavy rain [56].…”

Section: B Dataset Creationmentioning

confidence: 99%

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Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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“…All scenarios in the dataset are recorded in the night and during the winter period. Weather conditions in the recordings vary from clear weather, fog, and heavy rain [56].…”

Section: B Dataset Creationmentioning

confidence: 99%

“…Comparison of images taken in the clear weather and at the same distance with (a) standard thermal camera lens, (b) using telephoto lens[56].…”

mentioning

confidence: 99%

Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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“…Their dataset documents the research goal of controlling the pan-and-tilt of a thermal camera for human body segmentation of a 3D occupancy grid built from motion estimations, panoramic RGB images and depth information. Apart from that, existing thermal image datasets do not address SAR but other applications such as nighttime pedestrian detection (Xu et al, 2019) and surveillance (Krišto and Ivsić-Kos, 2019), where performance can be improved with multispectral combinations of color and thermal cameras, as in the KAIST dataset (Choi et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises

Morales

Vázquez-Martín

Mandow

et al. 2021

The International Journal of Robotics Research

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This article presents a collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. The sensor suite, applicable to unmanned ground vehicles (UGVs), consisted of overlapping visible light (RGB) and thermal infrared (TIR) forward-looking monocular cameras, a Velodyne HDL-32 three-dimensional (3D) lidar, as well as an inertial measurement unit (IMU) and two global positioning system (GPS) receivers as ground truth. Our mission was to collect a wide range of data from the SAR domain, including persons, vehicles, debris, and SAR activity on unstructured terrain. In particular, four data sequences were collected following closed-loop routes during the exercises, with a total path length of 5.2 km and a total time of 77 min. In addition, we provide three more sequences of the empty site for comparison purposes (an extra 4.9 km and 46 min). Furthermore, the data is offered both in human-readable format and as rosbag files, and two specific software tools are provided for extracting and adapting this dataset to the users’ preference. The review of previously published disaster robotics repositories indicates that this dataset can contribute to fill a gap regarding visual and thermal datasets and can serve as a research tool for cross-cutting areas such as multispectral image fusion, machine learning for scene understanding, person and object detection, and localization and mapping in unstructured environments. The full dataset is publicly available at: www.uma.es/robotics-and-mechatronics/sar-datasets .

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“…This work consists of two different experiments. The first network was fine-tuned on the thermal dataset UNIRI-TID [61] whereas the second model was trained from scratch on the same UNIRI-TID thermal dataset. The authors found that the model trained from scratch performed better than the fine-tuned model.…”

Section: Yolomentioning

confidence: 99%

Survey and Performance Analysis of Object Detection in Challenging Environments

Ahmed¹,

Ka²,

Pagani³

et al. 2021

Preprint

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Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

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Thermal Imaging Dataset for Person Detection

Cited by 34 publications

References 19 publications

Thermal Object Detection in Difficult Weather Conditions Using YOLO

Thermal Object Detection in Difficult Weather Conditions Using YOLO

The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises

Survey and Performance Analysis of Object Detection in Challenging Environments

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