Title :
Robust Video Object Segmentation Based on K-Means Background Clustering and Watershed in Ill-Conditioned Surveillance Systems
Author :
Chen, Tse-Wei ; Hsu, Shou-Chieh ; Chien, Shao-Yi
Author_Institution :
Nat. Taiwan Univ., Taipei
Abstract :
A robust video object segmentation algorithm for complex conditions in surveillance systems is proposed in this paper. This algorithm contains an unsupervised K-Means background clustering technique to model the temporal distribution in RGB domain for each spatial position. Based on the proposed background model, the object mask generation process integrates noise reduction, cast shadow cancellation, and improved watershed transform to obtain satisfying object masks. Experiments show that it can be applied on low-fame-rate and noisy video sequences in surveillance systems in which temporal tracking becomes impractical, and achieve better segmentation results than the previous works for complex lighting conditions and outdoor scenes.
Keywords :
feature extraction; image colour analysis; image segmentation; object detection; pattern clustering; transforms; unsupervised learning; video signal processing; video surveillance; RGB domain temporal distribution; cast shadow cancellation; ill-conditioned surveillance systems; noise reduction; object mask generation process; robust video object segmentation algorithm; shape extraction; unsupervised K-means background clustering; unsupervised background training technique; watershed transform; Clustering algorithms; Humans; Intelligent systems; Layout; Noise cancellation; Noise reduction; Noise robustness; Object segmentation; Surveillance; Video sequences;
Conference_Titel :
Multimedia and Expo, 2007 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-1016-9
Electronic_ISBN :
1-4244-1017-7
DOI :
10.1109/ICME.2007.4284768