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
Unsupervised Video Analysis for Counting of Wood in River during Floods
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Multimodal evaluation for medical image segmentation
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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
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Rivers during floods bring a lot of fallen trees and debris. Video surveillance systems are installed on strategically important places on the rivers. To protect these places from destructions due to accumulation of wood, such systems must be able to automatically detect wood. Image segmentation is performed to separate wood and other moving elements from the rest of the water. Moving objects are detected with respect to brightness and temporal variation features. The floating wood is then tracked in the sequence of frames by temporal linking of the segments generated in the detection step. Our algorithm is tested on multiple videos of floods and the results are evaluated both qualitatively and quantitatively.