Theoretical Advances

Pattern Analysis and Applications

, Volume 10, Issue 4, pp 291-299

First online:

Salient human detection for robot vision

  • Sooyeong KwakAffiliated withDepartment of Computer Science, Yonsei University
  • , ByoungChul KoAffiliated withDepartment of Computer Engineering, Keimyung University
  • , Hyeran ByunAffiliated withDepartment of Computer Science, Yonsei University Email author 

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In this paper, we propose a salient human detection method that uses pre-attentive features and a support vector machine (SVM) for robot vision. From three pre-attentive features (color, luminance and motion), we extracted three feature maps and combined them as a salience map. By using these features, we estimated a given object’s location without pre-assumptions or semi-automatic interaction. We were able to choose the most salient object even if multiple objects existed. We also used the SVM to decide whether a given object was human (among the candidate object regions). For the SVM, we used a new feature extraction method to reduce the feature dimensions and reflect the variations of local features to classifiers by using an edged-mosaic image. The main advantage of the proposed method is that our algorithm was able to detect salient humans regardless of the amount of movement, and also distinguish salient humans from non-salient humans. The proposed algorithm can be easily applied to human robot interfaces for human-like vision systems.


Salient human detection Pre-attentive features Support vector machine Color map Luminance map Motion map