Predicting consumers' locations in dynamic environments via 3D sensor-based tracking

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    1 Citation (Scopus)

    Abstract

    Brick-and-mortar stores for non-food items allow customers to quickly try items and take them home, but lack certain convenient features of online shopping, such as personalised offers and recommendations for items of possible interest. Mobile in-store shopping applications would allow to combine these advantages if they would derive current customer needs from customer activities. A natural way to infer interests of shops' visitors is to analyse their motion and places where they stop. This paper presents a low-cost depth sensor -- based people tracking system and a method to predict future customer locations, developed for environments where items are frequently re-located and customer routes change accordingly. The tracking system employs adaptive background modelling approach, allowing to quickly distinguish between moving humans and re-located objects. Similarity-based location predictions use fairly small datasets of recent tracks of other customers and allow predicting locations of future stops very soon after monitoring starts: after just a few minutes. Therefore this approach also provides for imperfect tracking, e.g., due to occlusions. In the tests with the data, acquired in a real clothing and cosmetics department during 50 days, future places of customers' interest were predicted with an average accuracy 60% and an average distance error half a metre.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationEighth International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages100-105
    ISBN (Electronic)978-1-4799-5073-7
    ISBN (Print)978-1-4799-5072-0
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    Event8th International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014 - Oxford, United Kingdom
    Duration: 10 Sep 201412 Sep 2014
    Conference number: 8th

    Conference

    Conference8th International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014
    Abbreviated titleNGMAST 2014
    CountryUnited Kingdom
    CityOxford
    Period10/09/1412/09/14

      Fingerprint

    Keywords

    • consumr behaviour
    • image sensor
    • mobile computing
    • object tracking
    • retail data processing

    Cite this

    Vildjiounaite, E., Mäkelä, S-M., Järvinen, S., Keränen, T., & Kyllönen, V. (2014). Predicting consumers' locations in dynamic environments via 3D sensor-based tracking. In Proceedings: Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014 (pp. 100-105). IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/NGMAST.2014.11