Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison

By Alejandro González, Zhijie Fang, Yainuvis Socarrás Salas, Joan Serrat, David Vázquez, Jiaolong Xu, Antonio M. López. Italian National Conference on Sensors, 2016

313 citations recorded by the source corpus.

Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images; (b) just infrared images; and (c) both of them. In order to obtain results for the last item, we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset that we have built for this purpose as well as on the publicly available KAIST multispectral dataset.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/330bcf952a5a20aac0e334aad1de4cd6ba6ed6eb

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Citations in the corpus

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection (2018, background): “Among these sensors, thermal cameras are the most widely used [12, 13, 14, 15], due to their visibility of pedestrian instances in challenging illumination conditions.”
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions (2021, unknown): “In addition, they can be used for warm body detection, such as pedestrians and animals [26–29].”
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection (2018, background): “To overcome the aforementioned limita tions, many research works have been focused on the development of multispectral pedestrian detection solutions to fa cilit te robust human target detection for around-the-clock applic ation [22, 21, 34, 28, 16, 13].”
Multispectral Deep Neural Networks for Pedestrian Detection (2016, background): “Nevertheless, except very recent effort [12, 42], most of previous studies focus on detecting pedestrians with color or thermal images only, rather than leveraging color and thermal images simultaneously.”
Visible and Infrared Image Fusion Using Deep Learning (2023, background): “For example, CVC-14 [180] and FLIR [181] provide visible-infrared image pairs for driving scenarios.”
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime (2018, background): “Far-infrared (FIR) cameras can be another choice [28], [9].”