计算机科学
人工智能
计算机视觉
帧(网络)
立体视觉
汽车工业
感知
多样性(控制论)
帧速率
立体摄像机
立体摄像机
深度学习
电信
神经科学
工程类
生物
航空航天工程
作者
Carlos Guindel,David Martín,José María Armingol
标识
DOI:10.1016/j.robot.2018.11.010
摘要
In this paper, we propose an efficient approach to perform recognition and 3D localization of dynamic objects on images from a stereo camera, with the goal of gaining insight into traffic scenes in urban and road environments. We rely on a deep learning framework able to simultaneously identify a broad range of entities, such as vehicles, pedestrians or cyclists, with a frame rate compatible with the strict requirements of onboard automotive applications. Stereo information is later introduced to enrich the knowledge about the objects with geometrical information. The results demonstrate the capabilities of the perception system for a wide variety of situations, thus providing valuable information for a higher-level understanding of the traffic situation.
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