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Communication Dans Un Congrès Année : 2007

Automatic People Detection and Counting for Athletic Videos Classification

Résumé

We propose a general framework that focuses on automatic individual/multiple people motion-shape analysis and on suitable features extraction that can be used on action/activity recognition problems under real, dynamical and unconstrained environments. We have considered various athletic videos from a single uncalibrated, possibly moving camera in order to evaluate the robustness of the proposed method. We have used an easily expanded hierarchical scheme in order to classify them to videos of individual and team sports. Robust, adaptive and independent from the camera motion, the proposed features are combined within Transferable Belief Model (TBM) framework providing a two level (frames and shot) video categorization. The experimental results of 97% individual/team sport categorization accuracy, using a dataset of more than 250 videos of athletic meetings indicate the good performance of the proposed scheme.
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Dates et versions

hal-00189773 , version 1 (22-11-2007)

Identifiants

  • HAL Id : hal-00189773 , version 1

Citer

Costas Panagiotakis, Emmanuel Ramasso, Georgios Tziritas, Michèle Rombaut, Denis Pellerin. Automatic People Detection and Counting for Athletic Videos Classification. AVSS 2007 - IEEE Advanced Video and Signal based Surveillance, Sep 2007, Londres, United Kingdom. pp.978-1-4244-1696-7/07. ⟨hal-00189773⟩
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