Real time segmentation of scene into objects and background is really important and represents an initial step of object tracking. Starting from the codebook method [4] we propose some modifications which show significant improvements in most of the normal and also difficult conditions. We include parameter of frequency for accessing, deleting, matching and adding codewords in codebook or to move cache codewords into codebook. We also propose an evaluation method in order to objectively compare several segmentation techniques, based on receiver operating characteristic (ROC) analysis and on precision and recall method. We propose to summarize the quality factor of a method by a single value based on a weighted Euclidean distance or on a harmonic mean between two related characteristics.
Object re-identification and tracking in nonoverlapping cameras is a challenging problem due to the variation of the object's appearance, linked to the different view angle, distance and color variation in different cameras. We present a computationally efficient real time human tracking algorithm, which can track objects inside the field of view (FOV) of a camera, re-identify objects that exit and then return in a same or in a different camera FOV. Object appearance in several cameras may be very different due to illumination conditions, camera gain, focus, focal length etc. Therefore inter-camera color calibration is important for object re-identification before applying object recognition features. We compare different existing color calibration methods and evaluate their color brightness transfer function (BTF) using Receiver Operating Characteristic (ROC) curve. We propose some modifications in cumulative brightness transfer function (CBTF) which significantly improve the objects re-identification in non-overlapping multi camera environment.
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