Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and segmentation of moving objects in complex scenes
Computer Vision and Image Understanding
Layered Video Objects Detection Based on LBP and Codebook
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
Non-parametric statistical background modeling for efficient foreground region detection
Machine Vision and Applications
IEEE Transactions on Intelligent Transportation Systems
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Texture and color information as important characters of video objects have been widely used in the detection of moving objects. A detection algorithm based on texture may cause detection errors in regions of blank texture and heterogeneous texture, and a detection algorithm based on color is easily influenced by illumination changes and shadows. In this paper, a new detection and fusion algorithm is proposed. At the detection stage based on texture, the background texture is classified according to the steering kernel. At the fusion stage, for the moving objects detected on the basis of texture and color respectively, a scheme based on a boundary selection strategy is proposed for combining the different detection objects. A relatively smooth boundary is selected as the true boundary, and the shadow detection is carried out to assist the boundary selection. Experimental results verify the advantages of the proposed algorithm as compared to the existing state-of-the-art algorithms.