TY - GEN
T1 - Non-parametric background and shadow modeling for object detection
AU - Tanaka, Tatsuya
AU - Shimada, Atsushi
AU - Arita, Daisaku
AU - Taniguchi, Rin Ichiro
PY - 2007
Y1 - 2007
N2 - We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.
AB - We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=38149077423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149077423&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76386-4_14
DO - 10.1007/978-3-540-76386-4_14
M3 - Conference contribution
AN - SCOPUS:38149077423
SN - 9783540763857
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 168
BT - Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 8th Asian Conference on Computer Vision, ACCV 2007
Y2 - 18 November 2007 through 22 November 2007
ER -