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.
CITATION STYLE
Kümmerle, J., Hinzmann, T., Vempati, A. S., & Siegwart, R. (2016). Real-time detection and tracking of multiple humans from high bird’s-eye views in the visual and infrared spectrum. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10072 LNCS, pp. 545–556). Springer Verlag. https://doi.org/10.1007/978-3-319-50835-1_49
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