Abstract
An approach for the semantic interpretation of image-based novelty in real-world environments is presented. We measure novelty using the concept of pixel-based surprise, which quantifies how much a new observation changes the robot’s current probabilistic appearance model of the environment. The corresponding surprise maps are utilized as prior information to reduce the search space of a “Histograms of Oriented Gradients” object detector. Specifically, detection windows are scored and selected using surprise values. Several object classes are simultaneously searched for and learned from a low number of manually taken reference images. Experiments are performed on a human-size robot in a cluttered household environment. Compared to object detection based on a search of the complete image, a 35-fold speed-up is observed. Additionally, the detection performance increases significantly.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, San Diego, CA, USA (June 2005)
Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 37–47. Springer, Heidelberg (2009)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, Kauai, Hawaii, USA (December 2001)
Maier, W., Mair, E., Burschka, D., Steinbach, E.: Visual homing and surprise detection for cognitive mobile robots using image-based environment representations. In: Proc. ICRA, Kobe, Japan (May 2009)
Maier, W., Steinbach, E.: A probabilistic appearance representation and its application to surprise detection in cognitive robots. IEEE Trans. on Autonomous Mental Development 2(4), 267–281 (2010)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC, London, United Kingdom (September 2009)
Chen, Y., Lin, C.: Combining SVMs with Various Feature Selection Strategies. In: Feature Extraction. STUDFUZZ, vol. 207, pp. 315–324. Springer, Heidelberg (2006)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vision Research 49(10), 1295–1306 (2009)
Ranganathan, A., Dellaert, F.: Bayesian surprise and landmark detection. In: Proc. ICRA, Kobe, Japan (May 2009)
Maier, W., Eschey, M., Steinbach, E.: Image-based object detection under varying illumination in environments with specular surfaces. In: Proc. ICIP, Brussels, Belguim (September 2011)
Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011)
Shum, H., Chan, S., Kang, S.: Image-Based Rendering. Springer, New York (2007)
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Alt, N., Maier, W., Rao, Q., Steinbach, E. (2012). Semantic Interpretation of Novelty in Images Using Histograms of Oriented Gradients. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_42
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DOI: https://doi.org/10.1007/978-3-642-33503-7_42
Publisher Name: Springer, Berlin, Heidelberg
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