Multi-target tracking in unevenly illuminated scenes

Visual tracking under non-uniform illumination is challenging as the appearance of a target may change across the scene, while it is being tracked. To account for this, a built-in color correction can be used to transform local tracking observations into a globally normalized color space, hereby compensating for the uneven illumination affecting the tracking. In a static environment a parametric model of such correction can be calibrated to the scene illumination off-line, by using a color chart, but in practical applications this may not be feasible. In this research we present methods to obtain such correction without requiring a calibration pattern be placed in the environment, instead, we use observations of people moving around in the scene as illumination probes naturally collected with a detector. The learning is always carried out in an unsupervised manner with different methods, and in the final part of the of this research we proposed a data association step to group detections into tracklets, and the color correction parameters are then found by optimizing appearance similarity within tracklets. Reported experiments for each method show that our methods are able to effectively learn the color correction with multiple people to achieve robust tracking in unevenly illuminated scene.