Abstract
Pedestrian parcel inspection is a common security measure in some public places like railway entrances. Automatic identification of the affiliation between pedestrians and parcels is an important task in an intelligent security inspection system. However, it is very challenging due to the high pedestrian volume in these places. In this paper, we propose a deep learning scheme for extracting pedestrian-parcel tuples from camera videos, which includes three modules, i.e. detection, interaction and re-identification of pedestrians and parcels. We first detect pedestrians and parcels in each frame, and then discriminate the affiliation between pedestrians and parcels by interaction behavior analysis, finally discard the redundant affiliations by re-identification of pedestrians and parcels. In the interaction module, we propose a lightweight interaction model for discriminating the affiliation between pedestrians and parcels in a single RGB image. Experiments on a video data at a subway entrance validate the proposed approach.
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Acknowledgment
This work was supported by National Science and Technology Major Project of China (grant 2018AAA0100800), and Opening Foundation of National Engineering Laboratory for Intelligent Video Analysis and Application.
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Wu, T., Zhang, X., Duan, F. (2019). A Deep Learning Scheme for Extracting Pedestrian-Parcel Tuples from Videos. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_4
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