  This paper presents a method for improving any object tracking algorithm
based on machine learning. During the training phase, important trajectory
features are extracted which are then used to calculate a confidence value of
trajectory. The positions at which objects are usually lost and found are
clustered in order to construct the set of 'lost zones' and 'found zones' in
the scene. Using these zones, we construct a triplet set of zones i.e. three
zones: In/Out zone (zone where an object can enter or exit the scene), 'lost
zone' and 'found zone'. Thanks to these triplets, during the testing phase, we
can repair the erroneous trajectories according to which triplet they are most
likely to belong to. The advantage of our approach over the existing state of
the art approaches is that (i) this method does not depend on a predefined
contextual scene, (ii) we exploit the semantic of the scene and (iii) we have
proposed a method to filter out noisy trajectories based on their confidence
value.
