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Parcel Tracking by Detection in Large Camera Networks

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Pattern Recognition (GCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

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Abstract

Inside parcel distribution hubs, several tenth of up 100 000 parcels processed each day get lost. Human operators have to tediously recover these parcels by searching through large amounts of video footage from the installed large-scale camera network. We want to assist these operators and work towards an automatic solution. The challenge lies both in the size of the hub with a high number of cameras and in the adverse conditions. We describe and evaluate an industry scale tracking framework based on state-of-the-art methods such as Mask R-CNN. Moreover, we adapt a siamese network inspired feature vector matching with a novel feature improver network, which increases tracking performance. Our calibration method exploits a calibration parcel and is suitable for both overlapping and non-overlapping camera views. It requires little manual effort and needs only a single drive-by of the calibration parcel for each conveyor belt. With these methods, most parcels can be tracked start-to-end.

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Acknowledgments

This work was supported by the Central Innovation Programme for SMEs of the Federal Ministry for Economic Affairs and Energy of Germany under grant agreement number 16KN044302.

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Authors and Affiliations

  1. Department of Computer Science, Kiel University, Kiel, Germany

    Sascha Clausen, Claudius Zelenka, Tobias Schwede & Reinhard Koch

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  1. Sascha Clausen
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  2. Claudius Zelenka
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  3. Tobias Schwede
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  4. Reinhard Koch
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Corresponding author

Correspondence to Sascha Clausen .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Clausen, S., Zelenka, C., Schwede, T., Koch, R. (2019). Parcel Tracking by Detection in Large Camera Networks. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_7

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