Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

作者：Paul Rodríguez, Gustavo Chau · 年份：2017 · DOI：10.1109/iccvw.2017.218 · 被引用次数：17 · 研究领域：Video Surveillance and Tracking Methods、Advanced Vision and Imaging、Sparse and Compressive Sensing Techniques

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. We present a novel, fully incremental PCP algorithm, named incPCP-PTI, that is 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. Results on synthetic videos and CDNET2014 videos 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.