Abstract
This paper presents an implementation of the Multiple Hypothesis Tracking (MHT) algorithm in the Advanced Driver Assistance Systems (ADAS) context.The proposed algorithmuses laser data received from two front mounted sensors on a mobile vehicle. The algorithm was tested with simulated and real world data and shown to obtain a very good performance. Nonholonomic motion models were used to model the movement of road agents instead of the more traditional constant velocity/acceleration models. The use of the nonholonomic motion models allows to obtain not only the linear velocity, but also the steering angle of vehicles, improving this way the future prediction and handling of occlusions. The MHT algorithm possesses some well-known critical disadvantages due to its complexity and computational growth, in this work we circumvent these limitations in order to achieve real time performance in real work conditions.
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References
Tinne, D.: Rigorously Bayesian Multitarget Tracking and Localization. PhD thesis, K. U. Leuven, Leuven, Belgium (May 2010)
Santos, V., Almeida, J., vila, E., Gameiro, D., Oliveira, M., Pascoal, R., Sabino, R., Stein, P.: ATLASCAR - technologies for a computer assisted driving system on board a common automobile. In: IEEE International Conference on Intelligent Transportation Systems, Funchal, Portugal, pp. 1421–1427 (September 2010)
Blackman, S.: Multiple hypothesis tracking for multiple target tracking 19, 5–18 (January 2004)
Reid, D.: An algorithm for tracking multiple targets 24, 843–854 (December 1979)
Koch, W.: On bayesian MHT for well-separated targets in densely cluttered environment. In: IEEE International Radar Conference, pp. 323–328 (May 1995)
Blackman, S., Dempster, R., Busch, M., Popoli, R.: IMM/MHT solution to radar benchmark tracking problem 35, 730–738 (April 1999)
Danchick, R., Newnam, G.: Reformulating reid’s MHT method with generalised murty k-best ranked linear assignment algorithm. In: IEE Proc. on Radar, Sonar and Navigation, pp. 13–22 (February 2006)
Streller, D., Dietmayer, K., Sparbert, J.: Object tracking in traffic scenes with multi-hypothesis approach using laser range images. In: 8th World Congress on Intelligent Transport Systems (2001)
Streller, D., Dietmayer, K.: Object tracking and classification using a multiple hypothesis approach. In: IEEE Intelligent Vehicles Symposium, pp. 808–812 (June 2004)
Arras, K., Grzonka, S., Luber, M., Burgard, W.: 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, pp. 1710–1715 (2008)
Tsokas, N., Kyriakopoulos, K.: Multi-robot multiple hypothesis tracking for pedestrian tracking. Autonomous Robot 32, 63–79 (2012)
Tsokas, N.A., Kyriakopoulos, K.J.: A multiple hypothesis people tracker for teams of mobile robots. In: IEEE International Conference on Robotics and Automation, pp. 446–451 (2010)
Murty, K.G.: An algorithm for ranking all the assignments in order of increasing cost. Operations Research 16, 682–687 (1968)
Cox, I., Hingorani, S.: An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking 18, 138–150 (February 1996)
Cox, I., Hingorani, S.: On finding ranked assignments with application to multitarget tracking and motion correspondence 31, 486–489 (January 1995)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)
Streller, D., Furstenberg, K., Dietmayer, K.: Vehicle and object models for robust tracking in traffic scenes using laser range images. In: IEEE International Conference on Intelligent Transportation Systems, pp. 118–123 (2002)
Laumond, J.P.: Robot motion planning and control. Springer (1998)
Almeida, J., Santos, V.M.: Real time egomotion of a nonholonomic vehicle using LIDAR measurements. Journal of Field Robotics 30, 129–141 (2013)
Schubert, R., Richter, E., Wanielik, G.: Comparison and evaluation of advanced motion models for vehicle tracking. In: International Conference on Information Fusion, pp. 1–6 (2008)
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Almeida, J., Santos, V. (2014). Multi Hypotheses Tracking with Nonholonomic Motion Models Using LIDAR Measurements. In: Armada, M., Sanfeliu, A., Ferre, M. (eds) ROBOT2013: First Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-319-03413-3_20
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DOI: https://doi.org/10.1007/978-3-319-03413-3_20
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