Abstract
We present an approach for predicting continuous pedestrian trajectories over a time horizon of 2.5 s by means of polynomial least-squares approximation and multilayer perceptron (MLP) artificial neural networks. The training data are gathered from 1075 real urban traffic scenes with uninstructed pedestrians including starting, stopping, walking and bending in. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients. It is independent of sensor parameters such as cycle time and robust regarding noise. Approximation and prediction can be performed very efficiently. It only takes 35 \(\upmu \)s on an Intel Core i7 CPU. Test results show 28 % lower prediction errors for starting scenes and 32 % for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well.
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References
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. Wiley, New York (2001)
Chang, C.C., Lin, C.J.: Training \(\nu \)-support vector regression: theory and algorithms. Neural Comput. 14(8), 1959–1977 (2002)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 9 Dec 2015
Euro NCAP: Euro NCAP 2020 Roadmap (2015). http://euroncap.blob.core.windows.net/media/16472/euro-ncap-2020-roadmap-rev1-march-2015.pdf. Accessed 9 Dec 2015
Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: On-line motif detection in time series with SwiftMotif. Pattern Recognit. 42(11), 3015–3031 (2009)
Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: Online segmentation of time series based on polynomial least-squares approximations. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2232–2245 (2010)
Gensler, A., Gruber, T., Sick, B.: Fast approximation library. http://ies-research.de/Software. Accesed 17 Dec 2013
Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., Sick, B.: Pedestrian’s trajectory forecast in public traffic with artificial neural networks. In: Proccedings of the 22nd International Conference on Pattern Recognition (ICPR), pp. 4110–4115 (2014)
Goldhammer, M., Hubert, A., Köhler, S., Zindler, K., Brunsmann, U., Doll, K., Sick, B.: Analysis on termination of pedestrians’ gait at urban intersections. In: Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 1758–1763 (2014)
Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks. In: Proceedings of the SAI Intelligent Systems Conference, pp. 390–399 (2015)
Goldhammer, M., Strigel, E., Meissner, D., Brunsmann, U., Doll, K., Dietmayer, K.: Cooperative multi sensor network for traffic safety applications at intersections. In: Proceedings of the IEEE 15th International Conference onIntelligent Transportation Systems (ITSC), pp. 1178–1183 (2012)
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New York (2009)
Kalarot, R., Morris, J.: Comparison of fpga and gpu implementations of real-time stereo vision. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–15 (2010)
Koehler, S., Goldhammer, M., Bauer, S., Zecha, S., Doll, K., Brunsmann, U., Dietmayer, K.: Stationary detection of the pedestrian’s intention at intersections. Intell. Transp. Syst. Mag., IEEE 5(4), 87–99 (2013)
Naujoks, F.: How Should I Inform my Driver? (2013). http://ko-fas.de/files/abschluss/ko-fas_c1_4_effective_advisory_warnings_based_on_cooperative_perception.pdf. Accessed 9 Dec 2015
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Winter, D.A.: Human balance and posture control during standing and walking. Gait Posture 3(4), 193–214 (1995)
World Health Organization: Global Status Report on Road Safety 2013: Supporting a Decade of Action (2013). http://www.who.int/violence_injury_prevention/road_safety_status/2013/en. Accessed 9 Dec 2015
Acknowledgments
This work partially results from the project AFUSS, supported by the German Federal Ministry of Education and Research (BMBF) under grant number 03FH021I3, and the project DeCoInt\(^2\), supported by the German Research Foundation (DFG) within the priority program SPP 1835: “Kooperativ Interagierende Automobile”, grant numbers DO 1186/1-1 and SI 674/11-1.
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Goldhammer, M., Köhler, S., Doll, K., Sick, B. (2016). Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_13
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