Illumination Invariant L1 Tracker Using Photometric Normalization Techniques

Recently, sparse representation-based tracking methods called l₁ trackers give remarkable performances in difficult video sequences. However, the tracking in the situation of large illumination changes and shadow casting still has serious problems that need to be solved. A new illumination invariant tracking method based on photometric normalization techniques and sparse representation framework is proposed. By using photometric normalization methods, we create a new illumination invariant template presentation for tracking and eliminate the effect of brightness variation and shadow casting. For enhancing the tracking accuracy, a method for adaptively selecting the optimal template presentation at the update step of the tracking process is introduced. The experiments show that our method outperforms the previous l₁ tracker and some state-of-the-art tracking algorithms in challenging tracking sequences.