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Cluster-Based SJPDAFs for Classification and Tracking of Multiple Moving Objects

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Field and Service Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 105))

Abstract

This paper describes a method for classifying and tracking multiplemoving objects with a laser range finder (LRF). As moving objects are tracked in the framework of sample-based joint probabilistic data association filters (SJPDAFs), the proposed method is robust against occlusions or false segmentation of LRF scans. It divides tracking targets and corresponding LRF segments into clusters and able to classify each cluster as a car or a group of pedestrians. In addition, it can correct false segmentation of LRF scans. We implemented the proposed method and obtained experimental results demonstrating its effectiveness in outdoor environments and crowded indoor environments.

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References

  1. Bar-Shalom, Y.: Extension of the probabilistic data association filter to multi-target tracking. In: Proc. of the 5th Symposium on Nonlinear Estimation, pp. 16–21 (1974)

    Google Scholar 

  2. Reid, D.B.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control AC-24(6), 843–854 (1979)

    Article  Google Scholar 

  3. Lau, B., Arras, K.O., Burgard, W.: Multi-model hypothesis group tracking and group size estimation. International Journal of Social Robotics 2(1), 19–30 (2010)

    Article  Google Scholar 

  4. Ata ur Rehman, Naqvi, S.M., Mihaylovay, L., Chambers, J.A.: Clustering and a joint probabilistic data association filter for dealing with occlusions in multi-target tracking. In: Proc. of 16th International Conference on Information Fusion, FUSION 2013 (2013)

    Google Scholar 

  5. Song, X., Cui, J., Zhao, H., Zha, H., Shibasaki, R.: Laser-based tracking of multiple interacting pedestrians via on-line learning, pp. 92–105 (2013)

    Google Scholar 

  6. Vermaak, J., Doucet, A.: Maintaining multi-modality through mixture. In: Proc. of 9th IEEE International Conference on Computer Vision (ICCV 2003), pp. 1110–1116 (2003)

    Google Scholar 

  7. Kurazume, R., Yamada, H., Murakami, K., Iwashita, Y., Hasegawa, T.: Target tracking using SIR and MCMC particle filters by multiple cameras and laser range finders. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), pp. 3838–3844 (2008)

    Google Scholar 

  8. Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research 22, 99–117 (2003)

    Article  Google Scholar 

  9. Dietmayer, K., Sparbert, J., Streller, D.: Model based object classification and object tracking in traffic scenes from range images. In: Proc. of the IEEE Intelligent Vehicle Symposium IV (2001)

    Google Scholar 

  10. Zhao, H., Zhan, Q., Chiba, M., Shibasaki, R., Cu, J., Zha, H.: Moving object classification using horizontal laser scan data. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2009), pp. 2424–2430 (2009)

    Google Scholar 

  11. Mori, T., Sato, T., Noguchi, H., Shimosaka, M., Fukui, R., Sato, T.: Moving objects detection and classification based on trajectories of LRF scan data on a grid map. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pp. 2606–2611 (2010)

    Google Scholar 

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Correspondence to Naotaka Hatao .

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Hatao, N., Kagami, S. (2015). Cluster-Based SJPDAFs for Classification and Tracking of Multiple Moving Objects. In: Mejias, L., Corke, P., Roberts, J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-07488-7_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07487-0

  • Online ISBN: 978-3-319-07488-7

  • eBook Packages: EngineeringEngineering (R0)

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