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Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles
المؤلفون المشاركون
Zou, Huanxin
Hou, Yi
Zhang, Hong
Zhou, Shilin
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-11-09
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Efficient and robust visual localization is important for autonomous vehicles.
By achieving impressive localization accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention of people in several research communities including autonomous vehicles.
Such an approach relies heavily on the outstanding discrimination power of ConvNet features to match detected landmarks between images.
However, a major challenge of this approach is how to extract discriminative ConvNet features efficiently.
To address this challenging, inspired by the high efficiency of the region of interest (RoI) pooling layer, we propose a Multiple RoI (MRoI) pooling technique, an enhancement of RoI, and a simple yet efficient ConvNet feature extraction method.
Our idea is to leverage MRoI pooling to exploit multilevel and multiresolution information from multiple convolutional layers and then fuse them to improve the discrimination capacity of the final ConvNet features.
The main advantages of our method are (a) high computational efficiency for real-time applications; (b) GPU memory efficiency for mobile applications; and (c) use of pretrained model without fine-tuning or retraining for easy implementation.
Experimental results on four datasets have demonstrated not only the above advantages but also the high discriminating power of the extracted ConvNet features with state-of-the-art localization accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hou, Yi& Zhang, Hong& Zhou, Shilin& Zou, Huanxin. 2017. Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189210
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hou, Yi…[et al.]. Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles. Mobile Information Systems No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1189210
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hou, Yi& Zhang, Hong& Zhou, Shilin& Zou, Huanxin. Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189210
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1189210
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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