Research

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Anomaly understanding visual attention

Objective

To develop a novel technique system for anomaly detection in the natural environment.


Description

The research focuses on small objects detection in the natural environment using the enhance visual attention based saliency detection technique. The proposed detection scheme is a fusion of two major algorithms, namely the fast edge detection method based on structured random forest and the saliency detection model based on wavelet transform. The combination of both strategies is intended to enhance the result of saliency detection, particularly for the detection of a small object.


Result                                   

                                                                                                                   Enhance visual attention based saliency detection technique.



Publications

  1. Y. Fang, W. Lin, Z. Chen, C-M Tsai, C-W Lin, “A Video Saliency Detection Model in Compressed Domain”, IEEE Transactions on circuits and systems for video technology, VOL. 24, NO. 1, pp. 27 - 38, 2014.
  2. Y. Yuan, Y. Fang, W. Lin, “Visual Object Tracking by Structure Complexity Coefficients”,IEEE Trans. Multimedia, accepted, 2015.
  3. Y. Yuan, S. Emmanuel, Y. Fang, W. Lin, “Visual Object Tracking based on Backward Model Validation”, IEEE Transactions on Circuits and Systems for Video Technology, VOL. 24, NO. 11, pp. 1898 - 1910, 2014.



Principal Investigators


Associate Prof. Lin Weisi (NTU)

Telephone: 6790 6651
Office: N4-02b-60
Email: wslin@ntu.edu.sg
Mr. Paul Tan (STE)

Telephone: 6660 1052
Office: S1-B4a-03
Email: paultan@stengg.com