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Real-Time Detection and Tracking of Multiple Humans from High Bird's-Eye Views in the Visual and Infrared Spectrum

机译：从视觉和红外光谱中的高鸟瞰图实时检测和跟踪多个人

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We propose a real-time system to detect and track multiple humans from high bird's-eye views. First, we present a fast pipeline to detect humans observed from large distances by efficiently fusing information from a visual and infrared spectrum camera. The main contribution of our work is a new tracking approach. Its novelty lies in online learning of an objectness model which is used for updating a Kalman filter. We show that an adaptive objectness model outperforms a fixed model. Our system achieves a mean tracking loop time of 0.8 ms per human on a 2GHz CPU which makes real time tracking of multiple humans possible.

机译：我们提出了一种实时系统，可以从高鸟瞰图检测并跟踪多个人。首先，我们提出了一种快速管道，可通过有效融合视觉和红外光谱摄像机的信息来检测从远距离观察到的人。我们工作的主要贡献是一种新的跟踪方法。其新颖性在于在线学习目标模型，该模型用于更新卡尔曼滤波器。我们证明了自适应目标模型优于固定模型。我们的系统在2GHz CPU上实现了每人0.8 ms的平均跟踪循环时间，这使得对多个人进行实时跟踪成为可能。

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《International symposium on visual computing》|2016年|545-556|共12页
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Julius Kummerle; Timo Hinzmann; Anurag Sai Vempati; Roland Siegwart;
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Real-Time Detection and Tracking of Multiple Humans from High Bird's-Eye Views in the Visual and Infrared Spectrum

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