Abstract
This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation or to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based on range and visual information which produces track-based labeling that is then employed to train supervised modules that perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change.
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References
ACFR (2006). PAATV/UTE projects (Technical Report). ACFR, Sydney TU, LCR, del Sur UN.
Attias, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of the fifteenth conference on uncertainty in artificial intelligence. San Francisco: Morgan Kaufmann.
Besl, P., & McKay, N. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256.
Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.
Bosch, A., Zisserman, A., & Munoz, X. (2007). Representing shape with a spatial pyramid kernel. In CIVR ’07: Proceedings of the 6th ACM international conference on image and video retrieval (pp. 401–408). Amsterdam: ACM.
Brooks, C. A., & Iagnemma, K. D. (2007). Self-supervised classification for planetary rover terrain sensing. In 2007 IEEE aerospace conference. Big Sky: IEEE.
Canny, J. F. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.
Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In ICML ’06: Proceedings of the 23rd international conference on machine learning. New York: ACM.
Chang, C., & Lin, C. (2008). A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
Cohen, I., Cozman, F. G., & Bronstein, A. (2002). The effect of unlabeled data on generative classifiers, with application to model selection. HP Laboratories Palo Alto.
Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., & Bradski, G. (2006). Self-supervised monocular road detection in desert terrain. In Robotics: science and systems. Cambridge: MIT Press.
DARPA (2007). DARPA urban challenge. http://www.darpa.mil/grandchallenge/.
Dempster, A., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1–38.
Duda, R., Hart, P., & Stork, D. (2001). Pattern classification. New York: Wiley.
Elfes, A. (1989). Occupancy grids: a probabilistic framework for robot perception and navigation. PhD thesis, Department of Electrical and Computer Engineering, Carnegie Mellon University.
Frank, O., Nieto, J., Guivant, J., & Scheding, S. (2003). Multiple target tracking using sequential Monte Carlo methods and statistical data association. In IEEE/RSJ international conference on intelligent robots and systems, IEEE, Las Vegas, USA.
Friedman, J. H., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28(2), 337–374.
Hoeting, J., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: a tutorial. Statistical Science, 14(4), 382–401.
Katz, R., Nieto, J., & Nebot, E. (2008). Probabilistic scheme for laser based motion detection. In IEEE/RSJ international conference on intelligent robots and systems (pp. 161–166). IEEE, Nice, France.
Lowe, D. (2004). Discriminative image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Luber, M., Arras, K. O., Plagemann, C., & Burgard, W. (2008). Classifying dynamic objects: an unsupervised learning approach. In Robotics: science and systems. Cambridge: MIT Press.
Luo, J., & Savakis, A. E. (2001). Self-supervised texture segmentation using complementary types of features. Pattern Recognition, 34(11), 2071–2082.
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002). Robust wide baseline stereo from maximally stable extremal regions. In British machine vision conference 2002, British Machine Vision Association.
McLachlan, G., & Krishnan, T. (1997). The EM algorithm and extensions. Wiley series in probability and statistics. New York: Wiley.
Moravec, H., & Elfes, A. (1985). High resolution maps from wide angle sonar. In International conference on robotics and automation, IEEE.
Ng, A., Jordan, M., & Weiss, Y. (2001). On spectral clustering: analysis and an algorithm. In NIPS: advances in neural information processing systems (Vol. 14).
Rabiner, L. R. (1990). A tutorial on hidden Markov Models and selected applications in speech recognition. In A. Waibel, & K. Lee (Eds.), Readings in speech recognition (pp. 267–296). San Mateo: Morgan Kaufmann.
Schultz, D. (2006). A probabilistic exemplar approach to combine laser and vision for person tracking. In Robotics: science and systems. Cambridge: MIT Press.
Serre, T., Wolf, L., & Poggio, T. (2005). Object recognition with features inspired by visual cortex. In International conference on computer vision and pattern recognition. San Diego: IEEE.
Simoncelli, E., & Freeman, W. (1995). The steerable pyramid: a flexible architecture for multi-scale derivative computation. In International conference on image processing (Vol. 3, pp. 444–447).
Stavens, D., & Thrun, S. (2006). A self-supervised terrain roughness estimator for off-road autonomous driving. In Conference on uncertainty in AI (UAI), Cambridge, MA.
Sun, Z., Bebis, G., & Miller, R. (2006). On-road vehicle detection: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 694–711.
Torralba, A., Murphy, K., Freeman, W. T., & Rubin, M. A. (2003). Context-based vision system for place and object recognition. In ICCV ’03: Proceedings of the 2003 ICCV international conference on computer vision, IEEE, Nice, France.
Tsuchiya, M., & Fujiyoshi, H. (2006). Evaluating feature importance for object classification in visual surveillance. In ICPR ’06: Proceedings of the 18th international conference on pattern recognition (pp. 978–981). Washington: IEEE Computer Society.
Viola, P., & Jones, M. (2004). Robust real-time object detection. International Journal of Computer Vision, 57, 2.
Weber, M., Welling, M., & Perona, P. (2000). Towards automatic discovery of categories. In CVPR ’00: Proceedings of the 2000 conference on computer vision and pattern recognition (CVPR ’00), IEEE Computer Society, Hilton Head Island, South Carolina.
Wu, T., Lin, C., & Weng, R. C. (2003). Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5, 975–1005.
Zelnik-Manor, L., & Perona, P. (2004). Self tuning spectral clustering. In NIPS: advances in neural information processing systems (Vol. 17).
Zhang, Q., & Pless, R. (2004). Extrinsic calibration for a camera and laser ranger finder (improves camera intrinsic calibration). In IEEE/RSJ international conference on intelligent robots and systems, IEEE, Japan.
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Katz, R., Nieto, J., Nebot, E. et al. Track-based self-supervised classification of dynamic obstacles. Auton Robot 29, 219–233 (2010). https://doi.org/10.1007/s10514-010-9193-0
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DOI: https://doi.org/10.1007/s10514-010-9193-0