Ruei-sung Lin, David Ross, Jongwoo Lim, Ming-Hsuan Yang
This paper presents an adaptive discriminative generative model that gen- eralizes the conventional Fisher Linear Discriminant algorithm and ren- ders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best sep- arates the target from the background. We present a computationally efficient algorithm to constantly update this discriminative model as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative genera- tive model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Nu- merous experiments show that our method is able to learn a discrimina- tive generative model for tracking target objects undergoing large pose and lighting changes.