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Link to original content: https://link.springer.com/doi/10.1007/s10514-011-9259-7
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Multi-robot multiple hypothesis tracking for pedestrian tracking

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Abstract

In this paper the problem of tracking walking people with multiple moving robots is tackled. For this purpose we present an adaptation to the Multiple Hypothesis Tracking method, which unlike classic MHT, allows for one-to-many associations between targets and measurements in each hypothesis production cycle and is thus capable of operating in a scenario involving multiple sensors. Derivation of hypotheses probabilities accounts for the continuously changing overlapping areas in fields of view of the robots sensors and for detection uncertainty. In the context of three experiments involving people walking among moving robots, the successful integration of our tracking algorithm to a real-world setup is assessed.

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References

  • Arras, K., Mozos, O., & Burgard, W. (2007). Using boosted features for the detection of people in 2d range data. In IEEE international conference on robotics and automation (pp. 3402–3407).

    Google Scholar 

  • Arras, K., Grzonka, S., Luber, M., & Burgard, W. (2008). Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In IEEE international conference on robotics and automation.

    Google Scholar 

  • Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP Journal on Image and Video Processing, Special Issue on Video Tracking in Complex Scenes for Surveillance Applications, 2008, Article ID 246309.

    Google Scholar 

  • Blackman, S. S. (2004). Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine. 19(1), 5–18.

    Article  Google Scholar 

  • Bruce, A., & Gordon, G. (2004). Better motion prediction for people tracking. In Proc. of the international conference on robotics and automation, New Orleans, USA.

    Google Scholar 

  • Carballo, A., Ohya, A., & Yuta, S. (2009). Multiple people detection from a mobile robot using double layered laser range finders. In Workshop on people detection and tracking.

    Google Scholar 

  • Cielniak, G., Bennewitz, M., & Burgard, W. (2003). Robust localization of persons based on learned motion patterns. In Proc. of the European conference on mobile robots.

    Google Scholar 

  • Cox, I., & Hirongani, S. (1996). An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2), 138–150.

    Article  Google Scholar 

  • Cui, J., Zha, H., Zhao, H., & Shibasaki, R. (2006). Laser-based interacting people tracking using multi-level observations. In Proc. of the 2006 IEEE/RSJ int. conf. on intelligent robots and systems.

    Google Scholar 

  • Cui, J., Zha, H., Zhao, H., & Shibasaki, R. (2008). Multi-modal tracking of people using laser scanners and video camera. Image and Vision Computing, 26(2), 240–252.

    Article  Google Scholar 

  • Dietmayer, K. C. J., Sparbert, J., & Streller, D. (2001). Model based object classification and object tracking in traffic scenes. IEEE Intelligent Vehicle Symposium, 25–30.

  • Fleuret, F., Berclaz, J., Lengagne, R., & Fua, P. (2008). Multi-camera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 267–282.

    Article  Google Scholar 

  • Fod, A., Howard, A., & Mataric, M. J. (2002). Laser-based people tracking. In Proc. of the IEEE international conference on robotics and automation (pp. 557–562).

    Google Scholar 

  • Iwata, K., Nakamura, K., Zhao, H., Shibasaki, R., & Takeuchi, H. (2006). Object detection with background occlusion modeling by using multiple laser range scanners. In Proceedings of the 6th international conference on ASIA GIS.

    Google Scholar 

  • Joo, S. W., & Chellappa, R. (2007). A multiple-hypothesis approach for multiobject visual tracking. IEEE Transactions on Image Processing, 16(11), 2849–2854.

    Article  MathSciNet  Google Scholar 

  • Kobilarov, M., Hyams, J., Batavia, P., & Sukhatme, G. S. (2006). People tracking and following with mobile robot using an omnidirectional camera and a laser. In IEEE international conference on robotics and automation (pp. 557–562).

    Google Scholar 

  • Lee, J. H., Tsubouchi, T., Yamamoto, K., & Egawa, S. (2006). People tracking using a robot in motion with laser range finder. In Proceedings of the 2006 IEEE/RSJ int. conf. on intelligent robots and systems (pp. 2936–2942).

    Chapter  Google Scholar 

  • Mendes, A., Bento, L. C., & Nunes, U. (2004). Multi-target detection and tracking with a laser scanner. IEEE Intelligent Vehicles Symposium, June, 796–801.

    Article  Google Scholar 

  • Prassler, E., Scholz, J., & Elfes, E. (1999). Tracking people in a railway station during rush-hour. In Proc. of 1st int. conf. on computer vision systems (pp. 162–179).

    Google Scholar 

  • Ramer, U. (1972). An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing, 1(2), 244–256.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Rekleitis, I. (2004). A particle filter tutorial for mobile robot localization. Technical report TR-CIM-04-02, Centre for Intelligent Machines, McGill University.

  • Schulz, D., Burgard, W., Fox, D., & Cremers, A. B. (2001). Tracking multiple moving objects with a mobile robot. Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 371–377.

    Google Scholar 

  • Smith, R. C., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 5(4), 56–68.

    Article  Google Scholar 

  • Stroupe, A., Martin, M., & Balch, T. (2000a). Merging gaussian distributions for object localization in multi-robot systems. In Proceedings of the seventh international symposium on experimental robotics.

    Google Scholar 

  • Stroupe, A., Martin, M., & Balch, T. (2000b). Merging probabilistic observations for mobile distributed sensing. Technical report CMU-RI-TR-00-30.

  • Taylor, G., & Kleeman, L. (2004). A multiple hypothesis walking person tracker with switched dynamic model. In Proc. of the Australasian conference on robotics and automation.

    Google Scholar 

  • Tsokas, N., & Kyriakopoulos, K. (2008). A multiple walking person tracker for laser-equipped mobile robots. In Proceedings of the 10th conference on intelligent autonomous systems.

    Google Scholar 

  • Tsokas, N., & Kyriakopoulos, K. (2010). A multiple hypothesis people tracker for teams of mobile robots. In Proceedings of the 2010 international conference on robotics and automation.

    Google Scholar 

  • Zhao, H., & Shibasaki, R. (2005). A novel system for tracking pedestrians using multiple single-row laser range scanners. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 35(2), 283–291.

    Article  Google Scholar 

  • Zhao, H., Chen, Y., Shao, X., Katabira, K., & Shibasaki, R. (2007). Monitoring a populated environment using single-row laser range scanners from a mobile platform. In IEEE international conference on robotics and automation (pp. 4739–4745).

    Google Scholar 

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Authors and Affiliations

  1. Control Systems Laboratory, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780, Zografou, Greece

    Nicolas A. Tsokas & Kostas J. Kyriakopoulos

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  1. Nicolas A. Tsokas
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  2. Kostas J. Kyriakopoulos
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Correspondence to Nicolas A. Tsokas.

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Tsokas, N.A., Kyriakopoulos, K.J. Multi-robot multiple hypothesis tracking for pedestrian tracking. Auton Robot 32, 63–79 (2012). https://doi.org/10.1007/s10514-011-9259-7

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