Skip to main content

Advertisement

Log in

Salient human detection for robot vision

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Scassellati B (2002) Theory of mind for a humanoid robot. Auton Robots 12:13–24

    Article  MATH  Google Scholar 

  2. Treisman A (1985) Preattentive processing in vision. Comp Vis Graph Image Process 31:156–177

    Article  Google Scholar 

  3. Zhao L, Charles CE (2000) Stereo and neural network-based pedestrian detection. IEEE Trans ITS 1:148–154

    Google Scholar 

  4. Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans PAMI 22:809-830

    Google Scholar 

  5. Papageorgiou C, Oren M, Poggio T (1998) A general framework for object detection. In: International conference on computer vision, pp 555–562

  6. Mohan A, Papageorgiou C, Poggio T (2001) Example-based object detection in images by components. IEEE Trans PAMI 23:349-361

    Google Scholar 

  7. Gavrila DM, Giebel J (2002) Shape-based pedestrian detection and tracking. Intelligent Vehicle Symp 1:8–14

    Google Scholar 

  8. Moghaddam B, Yang MH (2000) Gender classfication with support vector machines. In: IEEE international conference on automatic face and gesture recognition, pp 306-311

  9. Itti L, Koch C, Niebur E (2000) A model of saliency based visual attention for rapid scene analysis. IEEE Trans PAMI 20:1254–1259

    Google Scholar 

  10. Jung B, Sukhatme GS (2004) Detecting moving objects suing a single camera on a mobile robot in an outdoor environment. In: International conference on intelligent automomous systems, pp 980–987

  11. Viola P, Jones MJ, Snow D (2005) Detecting pedestrians using patterns of motion and appearance. Int J Comp Vis 63:153–161

    Article  Google Scholar 

  12. Oren M, Papageorgiou C, Sinha P, Osuna E, Poggio T (1997) Pedestrian detection using wavelet templates. In: International conference on computer vision and pattern recognition, pp 193–199

  13. Wolfe JM (1994) A revised model of visual search. Psychon Bull Rev 1:202–238

    Google Scholar 

  14. Lucas B, Kanade T (1987) An iterative image registration technique with an application to stereo vision. In: Proceedings of 7th international joint conference on artificial intelligence, pp 674-679

Download references

Acknowledgments

This research was supported by the Ministry of Information and Communication, Korea under the Information Technology Research Center support program supervised by the Institute of Information Technology Assessment, IITA-2005-(C1090-0501-0019).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyeran Byun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kwak, S., Ko, B. & Byun, H. Salient human detection for robot vision. Pattern Anal Applic 10, 291–299 (2007). https://doi.org/10.1007/s10044-007-0068-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-007-0068-8

Keywords

Navigation