Tracking technology

(Page still under construction)

Using overhead depth sensors as raw data source, we experimented on different technologies to perform pedestrian detection and tracking, going from off-line to real-time capabilities. 

Typically, we look at pedestrian tracking as a two-phase job: 

  1. per-frame pedestrian localization
  2. tracking

Most of our R&D went into point 1. while we outsourced point 2. to scientifc libraries, specifically

Although our localization methods are published in papers, our codes are, at least for now, not freely available. We are working on localization following two alternative approaches:

  • Hierarchical clustering of the depth cloud 
       see e.g.     A.Corbetta et al. Fluctuations around mean walking behaviours in diluted pedestrian flows. Phys. Rev. E, 95 , pp. 032316, 2017.  (publications )
  • Convolutional Neural Network-based analysis of the depth cloud
      see             A.Corbetta et al. Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields, AVSS17, 2017. (publications ) or this poster 
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