Abstract
Multiple obstacle detection is a challenging problem and has many important applications including video tracking, intelligence surveillance, robot navigation and autonomous driving. In existing methods, individual obstacle’s detection and contextual visual patterns are modeled separately and interactions from obstacles and their surroundings are mostly considered in a symmetric way, which we argue is not an optimal strategy. To tackle these difficulties, in this paper, we propose a deep convolutional networks for solving the online multiple obstacles detection problem. The method consists of deep visual information extraction and visual pattern learning. They are modeled as deep Convolution Neural Networks, which are able to learn discriminative visual features for obstacle detection and model inter-object relations in an asymmetric way and give the orientation extraction for the moving obstacles. The deep learning framework is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations and orientation information. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of the proposed method on the benchmarks demonstrate the effectiveness of our proposed framework.
Supported by the PhD Research startup Foundation of Liaocheng University (No. 318051654) and A Project of Shandong Province Higher Educational Science and Technology Program (No. KJ2018BAN109).
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Fan, Y., Zhou, L., Fan, L., Yang, J. (2019). Multiple Obstacle Detection for Assistance Driver System Using Deep Neural Networks. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_45
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