Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method. A new approach named Hermes is proposed, which exploits aspects from the well-known front propagation algorithms and compares favorably to them. Very promising experimental results are provided using real video sequences.