Efficient detection and tracking of salient regions for visual processing on mobile platforms

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Date

2009

Author

Serhat, Gülhan

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Visual Attention is an interesting concept that constantly widens its application areas in the field of image processing and computer vision. The main idea of visual attention is to find the locations on the image that are visually attractive. In this thesis, the visually attractive regions are extracted and tracked in video sequences coming from the vision systems of mobile platforms. First, the salient regions are extracted in each frame and a feature vector is constructed for each one. Then Scale Invariant Feature Transform (SIFT) is applied only to the salient regions to extract more stable features. The tracking is achieved by matching the salient regions of consecutive frames by comparing their feature vectors. Then the SIFT points of salient regions are matched to calculate the shift values for the matched pairs. Limiting the SIFT application to only the salient regions results in significantly reduced computational cost. Moreover, the salient region detection procedure is also limited to the predetermined regions throughout the video sequence in order to increase the efficiency. In addition, the visual attention channels are limited to the most dominant features of the regions. Experimental results that compare the algorithm outputs with ground-truth data reveal that, the proposed algorithm has fine tracking performance together with acceptable computational cost. Promising results are obtained even with blurred video sequences typical of ground vehicles and robots and in an uncontrolled environment.

Subject Keywords

Electrical engineering., Electronic computers.

URI

http://etd.lib.metu.edu.tr/upload/12611036/index.pdf
https://hdl.handle.net/11511/19045

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

G. Serhat, “Efficient detection and tracking of salient regions for visual processing on mobile platforms,” M.S. - Master of Science, Middle East Technical University, 2009.