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Multi Object Tracking Based on Detection with Deep Learning and Hierarchical Clustering

机译:基于深度学习和层次聚类的检测多目标跟踪

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Multi object tracking is one of the hotspots of computer vision research. Tracking-by-detection is a common approach to multi-object tracking. With the development of machine learning, especially of deep leaning method, the basis for a tracker becomes much more reliable. The method proposed in this paper is based on detection with convolutional neural network and tracking with hierarchical clustering. Experimental evaluation shows that the proposed method achieves overall competitive performance at high frame rates.
机译:多对象跟踪是计算机视觉研究的热点之一。通过检测进行跟踪是多对象跟踪的常用方法。随着机器学习的发展,尤其是深度学习方法的发展,跟踪器的基础变得更加可靠。本文提出的方法基于卷积神经网络检测和分层聚类跟踪。实验评估表明,所提出的方法在高帧频下可获得整体竞争性能。

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