Simultaneous fusion, classification, andtraction of moving obstacles by LIDAR and camera using Bayesian algorithm
DOI:
https://doi.org/10.15649/2346075X.997Palabras clave:
Mahalonobis Distance, Bayesian Algorithm, Simultaneous classification and traction, LIDAR sensor and cameraResumen
In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.
Referencias
V. Schmidlin, "Poursuite multicible multicapteur à l'aide de réseaux neuronaux. Application à la poursuite de cibles aériennes," Nice, 1994.
P. A. Samara, G. N. Fouskitakis, J. S. Sakellariou, and S. D. Fassois, "A statistical method for the detection of sensor abrupt faults in aircraft control systems," IEEE Transactions on Control Systems Technology, vol. 16, pp. 789-798, 2008.
https://doi.org/10.1109/TCST.2007.903109
X. Zhu, "Fundamentals of applied information theory," Beij ing: Tsinghua Universi ty Pres s, 2000.
M. Truchon, "Borda and the maximum likelihood approach to vote aggregation," Mathematical Social Sciences, vol. 55, pp. 96-102, 2008.
https://doi.org/10.1016/j.mathsocsci.2007.08.001
Z.-J. Zhou, C.-H. Hu, D.-L. Xu, J.-B. Yang, and D.-H. Zhou, "Bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of pipeline leak detection," Expert Systems with Applications, vol. 38, pp. 3937-3943, 2011.
https://doi.org/10.1016/j.eswa.2010.09.055
S.-H. Oh, "Improving the error backpropagation algorithm with a modified error function," IEEE Transactions on Neural Networks, vol. 8, pp. 799-803, 1997.
https://doi.org/10.1109/72.572117
Y. Deng, "Generalized evidence theory," Applied Intelligence, vol. 43, pp. 530-543, 2015.
https://doi.org/10.1007/s10489-015-0661-2
T. T. Nguyen, J. Spehr, D. Vock, M. Baum, S. Zug, and R. Kruse, "A general reliability-aware fusion concept using DST and supervised learning with its applications in multi-source road estimation," in 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 597-604.
https://doi.org/10.1109/IVS.2018.8500713
Z. Jing, M. Li, and H. Leung, "Multi-target joint detection, tracking and classification based on random finite set for aerospace applications," Aerospace Systems, vol. 1, pp. 1-12, 2018.
https://doi.org/10.1007/s42401-018-0003-2
P. Emami, P. M. Pardalos, L. Elefteriadou, and S. Ranka, "Machine learning methods for solving assignment problems in multi-target tracking," arXiv preprint arXiv:1802.06897, 2018.
K. Fang, Y. Xiang, X. Li, and S. Savarese, "Recurrent autoregressive networks for online multi-object tracking," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 466-475.
https://doi.org/10.1109/WACV.2018.00057
S. Wang, Z. Ge, G. Lv, and K. Lu, "Research on Multi-Target Stable Tracking Algorithm Based on Detection and Tracking Fusion," in Journal of Physics: Conference Series, 2018, p. 012175.
https://doi.org/10.1088/1742-6596/1069/1/012175
M. Dimitrievski, P. Veelaert, and W. Philips, "Behavioral pedestrian tracking using a camera and LiDAR sensors on a moving vehicle," Sensors, vol. 19, p. 391, 2019.
https://doi.org/10.3390/s19020391
J. Zhao, H. Xu, H. Liu, J. Wu, Y. Zheng, and D. Wu, "Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors," Transportation research part C: emerging technologies, vol. 100, pp. 68-87, 2019.
https://doi.org/10.1016/j.trc.2019.01.007
K.-H. Lee, Y. Kanzawa, M. Derry, and M. R. James, "Multi-Target Track-to-Track Fusion Based on Permutation Matrix Track Association," in 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 465-470.
https://doi.org/10.1109/IVS.2018.8500433
X. Ji, G. Zhang, X. Chen, and Q. Guo, "Multi-perspective tracking for intelligent vehicle," IEEE Transactions on Intelligent Transportation Systems, vol. 19, pp. 518-529, 2018.
https://doi.org/10.1109/TITS.2017.2784486
K. Yoon, D. Y. Kim, Y.-C. Yoon, and M. Jeon, "Data association for multi-object tracking via deep neural networks," Sensors, vol. 19, p. 559, 2019.
https://doi.org/10.3390/s19030559
N. M. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan, "Multi-object Tracking Cascade with Multi-Step Data Association and Occlusion Handling," in 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018, pp. 1-6.
https://doi.org/10.1109/AVSS.2018.8639321
P. Viola and M. Jones, "Robust real-time object detection," International journal of computer vision, vol. 4, p. 4, 2001.
D. Z. Wang and I. Posner, "Voting for Voting in Online Point Cloud Object Detection," in Robotics: Science and Systems, 2015, p. 10.15607.
A. Geiger, C. Wojek, and R. Urtasun, "Joint 3d estimation of obstacles and scene layout," in Advances in Neural Information Processing Systems, 2011, pp. 1467-1475.
C. Premebida, J. Carreira, J. Batista, and U. Nunes, "Pedestrian detection combining rgb and dense lidar data," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, pp. 4112-4117.
https://doi.org/10.1109/IROS.2014.6943141
A. González, G. Villalonga, J. Xu, D. Vázquez, J. Amores, and A. M. López, "Multiview random forest of local experts combining rgb and lidar data for pedestrian detection," in 2015 IEEE Intelligent Vehicles Symposium (IV), 2015, pp. 356-361.
https://doi.org/10.1109/IVS.2015.7225711
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, et al., "3d object proposals for accurate object class detection," in Advances in Neural Information Processing Systems, 2015, pp. 424-432.
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