TY - GEN
T1 - Moving Object Detection on RGB-D Videos Using Graph Regularized Spatiotemporal RPCA
AU - Javed, Sajid
AU - Bouwmans, Thierry
AU - Sultana, Maryam
AU - Jung, Soon Ki
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Moving object detection is the fundamental step for various computer vision tasks. Many existing methods are still limited in accurately detecting the moving objects because of complex background scenes such as illumination condition, color saturation, and shadows etc. RPCA models have shown potential for moving object detection, where input data matrix is decomposed into a low-rank matrix representing the background image and a sparse component identifying moving objects. However, RPCA methods are not ideal for real-time processing because of the batch processing issues. These methods also show a performance degradation without encoding spatiotemporal and depth information. To address these problems, we investigate the performance of online Spatiotemporal RPCA (SRPCA) algorithm [1] for moving object detection using RGB-D videos. SRPCA is a graph regularized algorithm which preserves the low-rank spatiotemporal information in the form of dual spectral graphs. This graph regularized information is then encoded into the objective function which is solved using online optimization. Experiments show competitive results as compared to four state-of-the-art subspace learning methods.
AB - Moving object detection is the fundamental step for various computer vision tasks. Many existing methods are still limited in accurately detecting the moving objects because of complex background scenes such as illumination condition, color saturation, and shadows etc. RPCA models have shown potential for moving object detection, where input data matrix is decomposed into a low-rank matrix representing the background image and a sparse component identifying moving objects. However, RPCA methods are not ideal for real-time processing because of the batch processing issues. These methods also show a performance degradation without encoding spatiotemporal and depth information. To address these problems, we investigate the performance of online Spatiotemporal RPCA (SRPCA) algorithm [1] for moving object detection using RGB-D videos. SRPCA is a graph regularized algorithm which preserves the low-rank spatiotemporal information in the form of dual spectral graphs. This graph regularized information is then encoded into the objective function which is solved using online optimization. Experiments show competitive results as compared to four state-of-the-art subspace learning methods.
UR - http://www.scopus.com/inward/record.url?scp=85041123980&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70742-6_22
DO - 10.1007/978-3-319-70742-6_22
M3 - Conference contribution
AN - SCOPUS:85041123980
SN - 9783319707419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 230
EP - 241
BT - New Trends in Image Analysis and Processing – ICIAP 2017 - ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Revised Selected Papers
A2 - Battiato, Sebastiano
A2 - Farinella, Giovanni Maria
A2 - Leo, Marco
A2 - Gallo, Giovanni
PB - Springer Verlag
T2 - 19th International Conference on Image Analysis and Processing, ICIAP 2017
Y2 - 5 June 2017 through 9 June 2017
ER -