Salient human detection for robot vision

Sooyeong Kwak, Byoungchul Ko, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)291-299
Number of pages9
JournalPattern Analysis and Applications
Issue number4
Publication statusPublished - 2007 Oct

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Salient human detection for robot vision'. Together they form a unique fingerprint.

Cite this