Abstract
This paper presents a novel approach of object detection and localization for service robots, which combines color-based and gradient-based detectors and automatically adapts the color model according to the variation of lighting conditions. Exploiting complementary visual features, the fusion of color-based and gradient-based detectors can achieve both robust detection and accurate localization. In real world environment, the color-based detection according to an offline-learned general model may fail. From a new linear color variation model proposed in this paper, our approach can generate a specific model for the target object in the image and achieve self-adaptation of color detector for robust detection and accurate localization. The experiments show that the proposed method can significantly increase the detection rate for target object in various real world environments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, vol. 1, pp. 886–893 (2005)
Diplaros, A., Gevers, T., Patras, I.: Combining color and shape information for illumination-viewpoint invariant object recognition. IEEE Trans on IP 15(1), 1–11 (2006)
Gevers, T., Stokman, H.: Robust histogram construction from color invariants for object recognition. PAMI 25(10), 1–6 (2003)
Javed, O., Ali, S., Shah, M.: Online detection and classification of moving objects using progressively improving detectors. In: CVPR 2005, vol. 1, pp. 696–701 (2005)
Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition 40(3), 1106–1122 (2007)
Khan, F.S., van de Weijer, J., Vanrell, M.: Top-down color attention for object recognition. In: ICCV 2009 (2009)
Li, L., Gu, I.Y.H., Leung, M.K.H., Tian, Q.: Adaptive background subtraction based on feedback from fuzzy classification. Optical Engineering 43, 2381–2394 (2004)
Limbu, D.K., et al.: Experiences with Brista Robot:FusionBot. In: Int’l Conf on Social Robotics, Korea, August 16–18 (2009)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int’l J. of Computer Vision 60(2), 91–110 (2004)
Stokman, H., Gevers, T.: Selection and fusion of colour models for image feature detection. PAMI 29(3), 371–381 (2007)
Tsymbal, A., Puuronen, S., Patterson, D.W.: Ensemble feature selection with the simple bayesian classification. Information Fusion 4, 87–100 (2003)
Zhou, Q., Ma, L., Chelberg, D.: Adaptive object detection and recognition based on a feedback strategy. Image and Vision Computing 24(1), 80–93 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, L., Yu, X., Li, L., Hoe, J.K.E. (2010). Fusion and Self-adaptation of Color and Gradient Based Models for Object Detection and Localization in Applications of Service Robots. In: Ge, S.S., Li, H., Cabibihan, JJ., Tan, Y.K. (eds) Social Robotics. ICSR 2010. Lecture Notes in Computer Science(), vol 6414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17248-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-17248-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17247-2
Online ISBN: 978-3-642-17248-9
eBook Packages: Computer ScienceComputer Science (R0)