274 research outputs found
Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges
Pedestrian detection has become a cornerstone for several high-level tasks,
including autonomous driving, intelligent transportation, and traffic
surveillance. There are several works focussed on pedestrian detection using
visible images, mainly in the daytime. However, this task is very intriguing
when the environmental conditions change to poor lighting or nighttime.
Recently, new ideas have been spurred to use alternative sources, such as Far
InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light
conditions. This study comprehensively reviews recent developments in low-light
pedestrian detection approaches. It systematically categorizes and analyses
various algorithms from region-based to non-region-based and graph-based
learning methodologies by highlighting their methodologies, implementation
issues, and challenges. It also outlines the key benchmark datasets that can be
used for research and development of advanced pedestrian detection algorithms,
particularly in low-light situation
Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning
Stable imaging in adverse environments (e.g., total darkness) makes thermal
infrared (TIR) cameras a prevalent option for night scene perception. However,
the low contrast and lack of chromaticity of TIR images are detrimental to
human interpretation and subsequent deployment of RGB-based vision algorithms.
Therefore, it makes sense to colorize the nighttime TIR images by translating
them into the corresponding daytime color images (NTIR2DC). Despite the
impressive progress made in the NTIR2DC task, how to improve the translation
performance of small object classes is under-explored. To address this problem,
we propose a generative adversarial network incorporating feedback-based object
appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module
and corresponding appearance consistency loss are proposed to reduce the
context dependence of object translation. As a representative example of small
objects in nighttime street scenes, we illustrate how to enhance the realism of
traffic light by designing a traffic light appearance loss. To further improve
the appearance learning of small objects, we devise a dual feedback learning
strategy to selectively adjust the learning frequency of different samples. In
addition, we provide pixel-level annotation for a subset of the Brno dataset,
which can facilitate the research of NTIR image understanding under multiple
weather conditions. Extensive experiments illustrate that the proposed FoalGAN
is not only effective for appearance learning of small objects, but also
outperforms other image translation methods in terms of semantic preservation
and edge consistency for the NTIR2DC task.Comment: 14 pages, 14 figures. arXiv admin note: text overlap with
arXiv:2208.0296
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Past to Present (P2P): Road Thermal Image Colorization
Thermal image colorization into realistic RGB image is a challenging task. Thermal cameras are easily to detect objects in particular situation (e.g. darkness and fog) that the human eyes cannot detect. However, it is difficult to interpret the thermal image with human eyes. Enhancing thermal image colorization is an important task to improve these areas. The results of the existing colorization method still have color ambiguities, distortion, and blurriness problems. This paper focused on thermal image colorization using pix2pix network architecture based on Generative Adversarial Net (GAN). Pix2pix is a model that transforms thermal image into RGB image, but our proposed model used three input types of images which are present as frame thermal image, present frame RGB image, and previous frame RGB image. By extracting the color information (i.e. luminance and chrominance) of the previous frame RGB image, the result obtained a more realistic RGB image. Experiments use two kinds of evaluation method, which are quantitative measure and qualitative measure. First, quantitative measure is the calculation of specific numerical scores, the method names are PSNR and SSIM. Second, qualitative measure is human subjective evaluation. Evaluation method compared and evaluated pix2pix and our proposed method with the two types of measuring method
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