Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensation

• Published in: IEEE transactions on image processing Vol. 13; no. 11; pp. 1473 - 1490
• Main Authors: Feghali, R., Mitiche, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Bayesian analysis; Bayesian methods; Boundaries; Cameras; Cluster Analysis; Computer Graphics; Computer Simulation; Detection, estimation, filtering, equalization, prediction; Estimates; Exact sciences and technology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image sequences; Information Storage and Retrieval - methods; Information, signal and communications theory; Level set; Mathematical analysis; Mathematical models; Models, Biological; Models, Statistical; Motion; Motion detection; Motion estimation; Movement - physiology; Numerical Analysis, Computer-Assisted; Object detection; Partial differential equations; Pattern recognition; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Spatiotemporal phenomena; Subtraction Technique; Telecommunications and information theory; Tracking; User-Computer Interface; Video Recording - methods; Walking - physiology; Target tracking; Moving target; Euler Lagrange equation; Motion estimation; Partition method; Image processing; Geometrical properties; Speed measurement; Displacement field; Shape detection; tracking; Partial differential equation; Implementation; Image sequence; Object detection; Induced field; Signal processing; Level-sets PDEs; Feature extraction; spatiotemporal analysis; Motion compensation; Motion detection; Edge detection; Optical flow
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2004.836158

The purpose of this study is to investigate a method of tracking moving objects with a moving camera. This method estimates simultaneously the motion induced by camera movement. The problem is formulated as a Bayesian motion-based partitioning problem in the spatiotemporal domain of the image sequence. An energy functional is derived from the Bayesian formulation. The Euler-Lagrange descent equations determine simultaneously an estimate of the image motion field induced by camera motion and an estimate of the spatiotemporal motion boundary surface. The Euler-Lagrange equation corresponding to the surface is expressed as a level-set partial differential equation for topology independence and numerically stable implementation. The method can be initialized simply and can track multiple objects with nonsimultaneous motions. Velocities on motion boundaries can be estimated from geometrical properties of the motion boundary. Several examples of experimental verification are given using synthetic and real-image sequences.