拥挤图像序列中鲁棒行人检测研究

Edgar Seemann, Mario Fritz, B. Schiele
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引用次数: 76

摘要

现实复杂场景中的目标分类检测一直是计算机视觉领域的一项具有挑战性的任务。最近的方法主要集中在对象类检测的单一通用模型上。然而,特别是在图像序列的背景下,为了在图像序列中可靠地检测该特定对象,将一般模型适应为更具体的对象实例模型可能是有利的。在这项工作中,我们提出了一个生成对象模型,它能够从一般对象类模型扩展到更具体的对象实例模型。这允许检测类实例以及可靠地区分单个对象实例。我们通过实验评估了该系统在静止图像和图像序列上的性能。
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Towards Robust Pedestrian Detection in Crowded Image Sequences
Object class detection in scenes of realistic complexity remains a challenging task in computer vision. Most recent approaches focus on a single and general model for object class detection. However, in particular in the context of image sequences, it may be advantageous to adapt the general model to a more object-instance specific model in order to detect this particular object reliably within the image sequence. In this work we present a generative object model that is capable to scale from a general object class model to a more specific object-instance model. This allows to detect class instances as well as to distinguish between individual object instances reliably. We experimentally evaluate the performance of the proposed system on both still images and image sequences.
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