Illumination independent change detection for real world image sequences
Computer Vision, Graphics, and Image Processing
Statistical model-based change detection in moving video
Signal Processing
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Selection and Fusion of Color Models for Feature Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Statistical change detection with moments under time-varying illumination
IEEE Transactions on Image Processing
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
We present a new noise model for color channels for statistical change detection. Based on this noise modeling, we estimate the distribution of Euclidean distances between the pixel colors of the background image and those of the foreground image. The optimal threshold for change detection is automatically determined using the estimated distribution. We show that our noise modeling is appropriate for various color spaces. Because the detection results differ according to the color space, we utilize the expected number of error pixels to select the appropriate color space for our method. Even if we detect changes based on the optimal threshold in a properly selected color space, there will inevitably be some false classifications. To reject these erroneous cases, we adopt graph cuts that efficiently minimize the global energy while taking into account the effect of neighboring pixels. To validate the proposed method, we show experimental results for a large number of images including indoor and outdoor scenes with complex clutter.