Panoramic Robust PCA for Foreground–Background Separation on Noisy, Free-Motion Camera Video

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Panoramic Robust PCA for Foreground–Background Separation on Noisy, Free-Motion Camera Video

Brian E. Moore; Chen Gao; Raj Rao Nadakuditi

This paper presents a new robust PCA method for foreground-background separation on freely moving camera video with possible dense and sparse corruptions. Our proposed method registers the frames of the corrupted video and then encodes the varying perspective arising from camera motion as missing data in a global model. This formulation allows our algorithm to produce a panoramic background component that automatically stitches together corrupted data from partially overlapping frames to reconstruct the full field of view. We model the registered video as the sum of a low-rank component that captures the background, a smooth component that captures the dynamic foreground of the scene, and a sparse component that isolates possible outliers and other sparse corruptions in the video. The low-rank portion of our model is based on a recent low-rank matrix estimator (OptShrink) that has been shown to yield superior low-rank subspace estimates in practice. To estimate the smooth foreground component of our model, we use a weighted total variation framework that enables our method to reliably decouple the true foreground of the video from sparse corruptions. We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions. Our experiments demonstrate the state-of-the-art performance of our proposed method compared to existing methods both in terms of foreground and background estimation accuracy.

SPS on Twitter

  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special Iss…
  • Join us tomorrow, Wednesday, 17 August at 10 AM Eastern as the SPS Webinar Series continues with Dr. Quiqiang Kong…
  • The 2023 IEEE membership year begins today, which means that new members can join now and receive service through 3…
  • On 15 September 2022, we are excited to partner with and to bring you a webinar and roundtable,…
  • The SPS Webinar Series continues on Monday, 22 August when Dr. Yu-Huan Wu and Dr. Shanghua Gao present “Towards Des…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar