Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Picture Processing
Detecting Persons on Changing Background
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Robust Histogram-Based Object Tracking in Image Sequences
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Robust human detection within a highly dynamic aquatic environment in real time
IEEE Transactions on Image Processing
Wood detection and tracking in videos of rivers
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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This paper presents a framework for counting the fallen trees, bushes and debris passing in the river by monocular vision. Automatic segmentation and recognition of wood in the river is relatively new field of research. Unsupervised segmentation of the wooden objects moving in the river has been developed. A novel method is developed for the separation of wood from water waves. The counting of number of fallen trees in the river is realized by tracking them in the consecutive continuous frames. The algorithm is tested on multiple videos of floods and the results are evaluated both qualitatively and quantitatively.