Abstract
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 2 GHz CPU which makes real time tracking of multiple humans possible.
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Acknowledgment
The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 600958 (SHERPA).
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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: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_49
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DOI: https://doi.org/10.1007/978-3-319-50835-1_49
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