Robust illumination-invariant tracking algorithm based on HOGs

Common tracking systems are usually affected by environmental and technical interferences such as non-uniform illumination, sensors' noise, geometrical scene distortion, etc. Among these issues, the former is particularly interesting because it destroys important spatial characteristics of objects in observed scenes. We propose a two-step tracking algorithm: first, preprocessing locally normalizes the illumination difference between the target object and observed frames; second, the normalized object is tracked by means of a designed template structure based on histograms of oriented gradients and kinematic prediction model. The algorithm performance is tested in terms of recognition and localization errors in real scenarios. In order to achieve high rate of the processing, we use GPU parallel processing technologies. Finally, our algorithm is compared with other state-of-the-art trackers.