Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods.
Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling