Issue No. 10 - October (2009 vol. 31)
Andreas Ess , ETH Zurich, Switzerland
Bastian Leibe , RWTH Aachen University, Germany
Konrad Schindler , TU Darmstadt, Germany
Luc van Gool , ETH Zurich, Switzerland and KU Leuven, Belgium
In this paper, we address the problem of multiperson tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. The complexity of the problem calls for an integrated solution that extracts as much visual information as possible and combines it through cognitive feedback cycles. We propose such an approach, which jointly estimates camera position, stereo depth, object detection, and tracking. The interplay between those components is represented by 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 robust tracking performance in scenes of realistic complexity.
Mobile vision, multiobject tracking, pedestrian detection, stereo depth, visual odometry, graphical model.
L. van Gool, A. Ess, B. Leibe and K. Schindler, "Robust Multiperson Tracking from a Mobile Platform," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 1831-1846, 2009.