Robust Multi-Person Tracking from Moving Platforms

Abstract

In this paper, we address the problem of multi-person tracking in busy pedestrian
  zones, using a stereo rig mounted on a mobile platform. The
  complexity of the problem calls for an integrated solution, which
  extracts as much visual information as possible and combines it
  through cognitive feedback. We propose such an approach, which
  jointly estimates camera position, stereo depth, object detection,
  and tracking. We model the interplay between these components 
  using a graphical model. Since the model has to
  incorporate object-object interactions, and temporal links to past
  frames, direct inference is intractable. We therefore propose a two-stage
  procedure: for each frame we first solve a simplified version of the
  model (disregarding interactions and temporal continuity) to
  estimate the scene geometry and an overcomplete set of object
  detections. Conditioned on these results, we then address object
  interactions, tracking, and prediction in a second step. The
  approach is experimentally evaluated on several long and difficult
  video sequences from busy inner-city locations. Our results show
  that the proposed integration makes it possible to deliver stable
  tracking performance in scenes of realistic complexity.