Illumination independent change detection for real world image sequences
Computer Vision, Graphics, and Image Processing
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Hand segmentation using learning-based prediction and verification for hand sign recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Thresholding for Change Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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The extraction of moving objects from image sequence of a static scene is an important task in computer vision. However, given an input image and the corresponding background image, due to the intersection of the foreground pixels and background pixels in the measurement space, it is challenging to identify a single threshold that can resolve the ambiguity in a satisfactory manner. In this paper, we propose a novel method for accurately classifying pixels into foreground and background based on two thresholded masks. Firstly, we produce two boundaries by automatically selecting two thresholds. Secondly, points are matched between these two boundaries by using dynamic time warping (DTW) algorithm and boundary segment pairs that are significantly different are identified. Thirdly, shadow, curvature and edge responses associated with each segment pair are evaluated for deducing the final boundary. Experimental results show that substantial improvement can be achieved by using the proposed method. Compared with other change detection methods, the proposed method produces more accurate and pleasing results.