Content-based Surveillance Retrieval over Foreground Objects
Date Issued
2009
Date
2009
Author(s)
Chang, Lun-Yu
Abstract
Video Surveillance is a hot research topic in computer vision, and there are lots of important issues under this scope, such as foreground segmentation, tracking, and image retrieval… etc. My research focus on two parts here, first, I proposed a new foreground segmentation method that fusion multiple modalities via graph cut, and speed up it with multi-core. Second, I build up a content-based surveillance retrieval system that using the new feature that I proposed.oreground detection is essential for semantic understanding and discovery for surveillance videos but still suffers from inefficiency and poor shape or silhouette detection. We argue to leverage multiple modalities (e.g., color appearance, foreground likelihood, spatial continuity, etc.) for foreground detection and propose a rigorous fusion method by graph cut. We further devise three strategies (e.g., dividing the graph cut problem into several subtasks, exploiting multi-core platform, etc.) to speed up the detection. Experimenting in open benchmarks, the proposed method outperforms other rival approaches in terms of detection accuracy and frame rate.owadays quality surveillance camera is cheaper than before, but how can we use an efficient way to extracting the information that we want is an important problem. If we want to search a thief in the cameras, we can easily get lots of surveillance video around this place. Then we will face to a problem how to find the thief in the whole videos. If we already have a query image, we can put it into a Content-based retrieval system that can easily transform the image into several low level features. Next the system would output a ranking list from the retrieval kernel. The main contributions of our system are proposing new features that try to enhance the retrieval result, even the query image captured in variety of resolutions (focal length) or position. In the experiments, we successfully improve the evaluation result when considering the features we proposed.
Subjects
foreground detection
foreground segmentation
graph cut
urveillance
multi-core
silhouette
surveillance retrieval
content-based
Type
thesis
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