Contextual Bayesian Inference for Visual Object Tracking and Abnormal Behavior Detection. (Suivi visuel d'objets et détection de comportements anormaux par inférence contextuelle Bayesienne)

Visual object tracking has been extensively investigated in the last two decades for its attractiveness and profitability. It remains an active area of research because of the lack of a satisfactory holistic tracking system that can deal with intrinsic and extrinsic distortions. Illumination variations, occlusions, noise and errors in object matching and classification are only a fraction of the problems currently encountered in visual object tracking. The work developed in this thesis integrates contextual information in a Bayesian framework for object tracking and abnormal behavior detection; more precisely, it focuses on the intrinsic characteristics of video signals in conjunction with object behavior to improve tracking outcomes. The representation of probability density functions is essential for modeling stochastic variables. In particular, parametric modeling is convenient since it makes possible the efficient storage of the representation and the simulation of the underlying stochastic process. The Gaussian mixture model is employed in this thesis to represent the pixel color distribution for segregation of foreground from background. The model adapts quickly to fast changes in illumination and resolves the problem of ``pixel saturation'' experienced by some existing background subtraction algorithms. The technique leads to better accuracy in the extraction of the foreground for higher-level tasks such as motion estimation. The solution of the Bayesian inference problem for Markov chains and, in particular, the well-known Kalman and particle filters is also investigated. The integration of contextual inference is of paramount importance in the aforementioned estimators; it results in object-specific tracking solutions with improved robustness. The vehicle tracking problem is explored in detail. The projective transformation, imposed by the environment configuration, is integrated into the Kalman and particle filters, which yields the ``projective Kalman filter'' and the ``projective particle filer''. Extensive experimental results are presented, which demonstrate that the projective Kalman and particle filters improve tracking robustness by reducing tracking drift and errors in the estimated trajectory. The constraint on the known nature of the environment is then relaxed to allow general tracking of pedestrians. A mixture of Gaussian Markov random fields is introduced to learn patterns of motion and model contextual information with particle filtering. Such inference results in an increased tracking robustness to occlusions. The local modeling with the Markov random fields also provides inference on abnormal behavior detection. Since local patterns are unveiled by the Markov random field mixture, detecting abnormal behavior is reduced to the matching of an object feature vector to the underlying local distribution, whereas the global approach, introducing generalization errors, involves complex, cumbersome and inaccurate decisions. Experimental evaluation on synthetic and real data show superior results in abnormal behavior detection for driving under the influence of alcohol and pedestrians crossing highways.