20 research outputs found
Saliency difference based objective evaluation method for a superimposed screen of the HUD with various background
The head-up display (HUD) is an emerging device which can project information
on a transparent screen. The HUD has been used in airplanes and vehicles, and
it is usually placed in front of the operator's view. In the case of the
vehicle, the driver can see not only various information on the HUD but also
the backgrounds (driving environment) through the HUD. However, the projected
information on the HUD may interfere with the colors in the background because
the HUD is transparent. For example, a red message on the HUD will be less
noticeable when there is an overlap between it and the red brake light from the
front vehicle. As the first step to solve this issue, how to evaluate the
mutual interference between the information on the HUD and backgrounds is
important. Therefore, this paper proposes a method to evaluate the mutual
interference based on saliency. It can be evaluated by comparing the HUD part
cut from a saliency map of a measured image with the HUD image.Comment: 10 pages, 5 fighres, 1 table, accepted by IFAC-HMS 201
What Is the Gaze Behavior of Pedestrians in Interactions with an Automated Vehicle When They Do Not Understand Its Intentions?
Interactions between pedestrians and automated vehicles (AVs) will increase
significantly with the popularity of AV. However, pedestrians often have not
enough trust on the AVs , particularly when they are confused about an AV's
intention in a interaction. This study seeks to evaluate if pedestrians clearly
understand the driving intentions of AVs in interactions and presents
experimental research on the relationship between gaze behaviors of pedestrians
and their understanding of the intentions of the AV. The hypothesis
investigated in this study was that the less the pedestrian understands the
driving intentions of the AV, the longer the duration of their gazing behavior
will be. A pedestrian--vehicle interaction experiment was designed to verify
the proposed hypothesis. A robotic wheelchair was used as the manual driving
vehicle (MV) and AV for interacting with pedestrians while pedestrians' gaze
data and their subjective evaluation of the driving intentions were recorded.
The experimental results supported our hypothesis as there was a negative
correlation between the pedestrians' gaze duration on the AV and their
understanding of the driving intentions of the AV. Moreover, the gaze duration
of most of the pedestrians on the MV was shorter than that on an AV. Therefore,
we conclude with two recommendations to designers of external human-machine
interfaces (eHMI): (1) when a pedestrian is engaged in an interaction with an
AV, the driving intentions of the AV should be provided; (2) if the pedestrian
still gazes at the AV after the AV displays its driving intentions, the AV
should provide clearer information about its driving intentions.Comment: 10 pages, 10 figure
Importance of Instruction for Pedestrian-Automated Driving Vehicle Interaction with an External Human Machine Interface: Effects on Pedestrians' Situation Awareness, Trust, Perceived Risks and Decision Making
Compared to a manual driving vehicle (MV), an automated driving vehicle lacks
a way to communicate with the pedestrian through the driver when it interacts
with the pedestrian because the driver usually does not participate in driving
tasks. Thus, an external human machine interface (eHMI) can be viewed as a
novel explicit communication method for providing driving intentions of an
automated driving vehicle (AV) to pedestrians when they need to negotiate in an
interaction, e.g., an encountering scene. However, the eHMI may not guarantee
that the pedestrians will fully recognize the intention of the AV. In this
paper, we propose that the instruction of the eHMI's rationale can help
pedestrians correctly understand the driving intentions and predict the
behavior of the AV, and thus their subjective feelings (i.e., dangerous
feeling, trust in the AV, and feeling of relief) and decision-making are also
improved. The results of an interaction experiment in a road-crossing scene
indicate that the participants were more difficult to be aware of the situation
when they encountered an AV w/o eHMI compared to when they encountered an MV;
further, the participants' subjective feelings and hesitation in
decision-making also deteriorated significantly. When the eHMI was used in the
AV, the situational awareness, subjective feelings and decision-making of the
participants regarding the AV w/ eHMI were improved. After the instruction, it
was easier for the participants to understand the driving intention and predict
driving behavior of the AV w/ eHMI. Further, the subjective feelings and the
hesitation related to decision-making were improved and reached the same
standards as that for the MV.Comment: 5 figures, Accepted by IEEE IV202
Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations
Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Small Object Detection (SOD) is an important machine vision topic because (i)
a variety of real-world applications require object detection for distant
objects and (ii) SOD is a challenging task due to the noisy, blurred, and
less-informative image appearances of small objects. This paper proposes a new
SOD dataset consisting of 39,070 images including 137,121 bird instances, which
is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The
detail of the challenge with the SOD4SB dataset is introduced in this paper. In
total, 223 participants joined this challenge. This paper briefly introduces
the award-winning methods. The dataset, the baseline code, and the website for
evaluation on the public testset are publicly available.Comment: This paper is included in the proceedings of the 18th International
Conference on Machine Vision Applications (MVA2023). It will be officially
published at a later date. Project page :
https://www.mva-org.jp/mva2023/challeng
Computational Models of Human Visual Attention and Their Implementations: A Survey
We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation
Mental Focus Analysis Using the Spatio-temporal Correlation between Visual Saliency and Eye Movements
The spatio-temporal correlation analysis between visual saliency and eye movements is presented for the estimation of the mental focus toward videos. We extract spatio-temporal dynamics patterns of saliency areas from the videos, which we refer to as saliency-dynamics patterns, and evaluate eye movements based on their correlation with the saliency-dynamics patterns in view. Experimental results using TV commercials demonstrate the effectiveness of the proposed method for the mental-focus estimation
Model-based Reminiscence : Guiding Mental Time Travel by Cognitive Modeling
HAI \u2716: The Fourth International Conference on Human Agent Interaction Biopolis Singapore October 4 - 7, 2016autho