Predicting and recognizing Interactions in Public Spaces

  • Fabio Poiesi and Andrea Cavallaro. Predicting and recognizing Interactions in Public Spaces. Journal of Real-Time Image Processing, 10(4):785-803, 2014. doi:10.1007/s11554-014-0428-8
    [BibTeX] [Abstract]

    We present an extensive survey of methods for recognizing human interactions and propose a method for predicting rendezvous areas in observable and unobservable regions using sparse motion information. Rendezvous areas indicate where people are likely to interact with each other or with static objects (e.g., a door, an information desk or a meeting point). The proposed method infers the direction of movement by calculating prediction lines from displacement vectors and temporally accumulates intersecting locations generated by prediction lines. The intersections are then used as candidate rendezvous areas and modeled as spatial probability density functions using Gaussian Mixture Models. We validate the proposed method to predict dynamic and static rendezvous areas on real-world datasets and compare it with related approaches.

    @Article{2014-05-POIESI,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Predicting and recognizing Interactions in Public Spaces}},
    journal = {{Journal of Real-Time Image Processing}},
    volume = {10},
    number = {4},
    pages = {785-803},
    date = {2014-05},
    doi = {10.1007/s11554-014-0428-8},
    abstract = {We present an extensive survey of methods for recognizing human interactions and propose a method for predicting rendezvous areas in observable and unobservable regions using sparse motion information. Rendezvous areas indicate where people are likely to interact with each other or with static objects (e.g., a door, an information desk or a meeting point). The proposed method infers the direction of movement by calculating prediction lines from displacement vectors and temporally accumulates intersecting locations generated by prediction lines. The intersections are then used as candidate rendezvous areas and modeled as spatial probability density functions using Gaussian Mixture Models. We validate the proposed method to predict dynamic and static rendezvous areas on real-world datasets and compare it with related approaches.},
    year = {2014}
    }

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