Benchmarking a large-scale FIR dataset for on-road pedestrian detection

Xu, Zhengzhuo; Zhuang, Jiajun; Liu, Qiong; Zhou, Jingkai; Peng, Shaowu

doi:10.1016/j.infrared.2018.11.007

Cited by 48 publications

(11 citation statements)

References 18 publications

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“…SCUT is a large far-infrared pedestrian detection dataset [ 46 ]. It consists of 11 h-long image sequences at a rate of 25 Hz generated by driving through diverse traffic scenarios at under 80 km/h.…”

Section: Methodsmentioning

confidence: 99%

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

Wang

et al. 2020

Sensors

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Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.

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Section: Methodsmentioning

confidence: 99%

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

Wang

et al. 2020

Sensors

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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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“…• SCUT (Xu et al 2019). KAIST ir SCUT yra reprezentatyviausi duomenų rinkiniai, skirti naudoti vairuotojų pagalbinėse sistemose.…”

Section: Pėsčiųjų Aptiktuvo Prototipo Tyrimaiunclassified

Improvement of intelligent methods for pedestrian detection in far-infrared radiation images

Tumas¹

2021

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Disertacija rengta 2016-2021 metais Vilniaus Gedimino technikos universitete. Vadovas prof. dr. Artūras SERACKIS Vilniaus Gedimino technikos universitetas, technologijos mokslai, elektros ir elektronikos inžinerija -T 001. Vilniaus Gedimino technikos universiteto Elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo taryba: Pirmininkas doc. dr. Vitalij NOVICKIJ (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001). Nariai: habil. dr. Pawel FORCZMANSKI (Ščecino Vakarų Pamario technologijos universitetas, Lenkija, elektros ir elektronikos inžinerija -T 001), prof. dr. Rytis MASKELIŪNAS (Kauno technologijos universitetas, informatikos inžinerija -T 007), doc. dr. Raimondas POMARNACKI (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001), prof. dr. Nerija ŽURAUSKIENĖ (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001). Disertacija bus ginama viešame Elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2021 m. liepos 8 d. 15 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje.

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Benchmarking a large-scale FIR dataset for on-road pedestrian detection

Cited by 48 publications

References 18 publications

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

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

Improvement of intelligent methods for pedestrian detection in far-infrared radiation images

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