Browsing by Author "Murase, Hiroshi"

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    Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations
    (JST, 2020) Sulistiyo, Mahmud Dwi; Kawanishi, Yasutomo; Deguchi, Daisuke; Ide, Ichiro; Hirayama, Takatsugu; Zheng, Jiang-Yu; Murase, Hiroshi; Computer and Information Science, School of Science
    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.
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    Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations
    (J-Stage, 2020) Sulistiyo, Mahmud Dwi; Kawanishi, Yasutomo; Deguchi, Daisuke; Ide, Ichiro; Hirayama, Takatsugu; Zheng, Jiang-Yu; Murase, Hiroshi; Computer and Information Science, School of Science
    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.
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    Sparse Coding of Weather and Illuminations for ADAS and Autonomous Driving
    (IEEE, 2018-06) Cheng, Guo; Zheng, Jiang Yu; Murase, Hiroshi; Computer and Information Science, School of Science
    Weather and illumination are critical factors in vision tasks such as road detection, vehicle recognition, and active lighting for autonomous vehicles and ADAS. Understanding the weather and illumination type in a vehicle driving view can guide visual sensing, control vehicle headlight and speed, etc. This paper uses sparse coding technique to identify weather types in driving video, given a set of bases from video samples covering a full spectrum of weather and illumination conditions. We sample traffic and architecture insensitive regions in each video frame for features and obtain clusters of weather and illuminations via unsupervised learning. Then, a set of keys are selected carefully according to the visual appearance of road and sky. For video input, sparse coding of each frame is calculated for representing the vehicle view robustly under a specific illumination. The linear combination of the basis from keys results in weather types for road recognition, active lighting, intelligent vehicle control, etc.