Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot
This paper proposes a novel method to detect objects by a mobile robot which adapts to an environment. Such a robot would help human designers of a smart environment to recognize objects in the environment with their attributes, which significantly facilitates his/her design. We first introduce Lifting Complex Wavelet Transform (LCWT) which plays an important role in this work. Since the LCWT has a set of controllable free parameters, we can design the LCWTs with various properties by tuning their parameters. In this paper we construct a set of LCWTs so that they can extract local features from an image by multi-scale. The extracted local features must be robust against several kinds of changes of the image such as shift, scale and rotation. Our method can design these LCWTs by selecting their parameters so that the mobile robot adapts to the environment. Applying the new set of LCWTs to the images captured by the mobile robot in the environment, a local feature database can be constructed. By using this database, we implement an object detection system based on LCWTs on the mobile robot. Effectiveness of our method is demonstrated by several test results using the mobile robot.
Keywordsadaptive object detection keypoint detection on-board robot vision visual words lifting complex wavelet transforms
Unable to display preview. Download preview PDF.
- 3.Fauqueur, J., Kingsbury, N., Anderson, R.: Multiscale Keypoint Detection using the Dual-Tree Complex Wavelet Transform. In: Proc. IEEE Conference on Image Processing, pp. 8–11 (2006)Google Scholar
- 4.Frintrop, S., Rome, E., Christensen, H.I.: Computational Visual Attention Systems and their Cognitive Foundation: A Survey. ACM Transactions on Applied Perception (TAP) 7(1) (2010)Google Scholar
- 5.Ijsselmuiden, J., Grosselfinger, A.-K., Münch, D., Arens, M., Stiefelhagen, R.: Automatic Behavior Understanding in Crisis Response Control Rooms. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds.) AmI 2012. LNCS, vol. 7683, pp. 97–112. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 7.Menegatti, E., Danieletto, M., Mina, M., Pretto, A., Bardella, A., Zanella, A., Zanuttigh, P.: Discovery, Localization and Recognition of Smart Objects by a Mobile Robot. In: Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., von Stryk, O. (eds.) SIMPAR 2010. LNCS, vol. 6472, pp. 436–448. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 8.Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object Retrieval with Large Vocabularies and Fast Spatial Matching. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8 (2007)Google Scholar
- 9.Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: Proc. Ninth IEEE International Conference on Computer Vision (ICCV 2003), vol. 2, pp. 1470–1477 (2003)Google Scholar
- 10.Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)Google Scholar
- 11.Takano, S., Suzuki, E.: New Object Detection for On-board Robot Vision by Lifting Complex Wavelet Transforms. In: Proc. Eleventh IEEE International Conference on Data Mining Workshops (ICDMW 2011), pp. 911–916 (2011)Google Scholar