A MACHINE-LEARNING-BASED ALGORITHM FOR DETECTING A MOVING OBJECT

Anmin Zhu and Yanming Chen

References

  1. [1] M. Matsumoto and S. Hashimoto, An optical device for close object detection, International Journal of Robotics and Automation, 26(2), 2011, 146–151.
  2. [2] R. Oliveira, F. Noguez, C. Costa, J. Barbosa, and M. Prado, Swtrack: An intelligent model for cargo tracking based on off-the-shelf mobile devices, Expert System with Applications, 40(6), 2013, 2023–2031.
  3. [3] A. Rozantsev, V. Lepetit, and P. Fua, On rendering synthetic images for training an object detector, Computer Vision and Image Understanding, 137, 2015, 24–37.
  4. [4] X. Cao and D. Zhu, Multi-auv underwater cooperative search algorithm based on biological inspired neurodynamics model and velocity synthesis, Journal of Navigation, 68(6), 2015, 1075–1087.
  5. [5] O. Barinova, V. Lempitsky, and P. Kholi, On detection of multiple object instances using hough transforms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 2012, 1773–1784.
  6. [6] J.-P. Hsiao, S.-S. Yeh, and P.-L. Hsu, Target position estimation using multi-vision system implemented on distributed mobile robots, International Journal of Robotics and Automation, 28(2), 2013, 154–169.
  7. [7] X. Zhou, C. Yang, and W. Yu, Moving object detection by detecting contiguous outliers in the low-rank representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), 2013, 597–610.
  8. [8] C.-C. Chen, S.-H. Chiu, J.-H. Lee, S.-Y. Chen, S.-H. Pong, and C.-P. Lu, A framework of barcode localization for mobile robots, International Journal of Robotics and Automation, 28(4), 2013, 317–330.
  9. [9] T. Brox, B. Rosenhahn, J. Gall, and D. Cremers, Combined region and motion-based 3d tracking of rigid and articulated objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 2010, 402–415.
  10. [10] Y. Chen, R. Zhang, L. Shang, and E. Hu, Object detection and tracking with active camera on motion vectors of feature points and particle filter, Review of Scientific Instruments, 84(6), 2013, 065109–065109–6.
  11. [11] R. Satzoda, S. Sathyanarayana, T. Srikanthan, and S. Sathyanarayana, Hierarchical additive hough transform for lane detection, IEEE Embedded Systems Letters, 2(2), 2010, 23–26.
  12. [12] A. Fakharian, S. Hosseini, and T. Gustafsson, Hybrid object detection using improved gaussian mixture model, in 11th International Conf. on Control, Automation and Systems, 1475–1479, 2011.
  13. [13] H. Zhang, X. Bai, J. Zhou, J. Cheng, and H. Zhao, Object detection via structural feature selection and shape model, IEEE Transactions on Image Processing, 22(12), 2013, 4984–4995.
  14. [14] Q. Xiao, N. Zhang, F. Li, and Y. Gao, Object detection based on combination of local and spatial information, Journal of Systems Engineering and Electronics, 22(4), 2011, 715–720.
  15. [15] X. Hong, H. Chang, S. Shan, B. zhong, X. Chen, and W. Gao, Sigma set based implicit online learning for object tracking, IEEE Signal Processing Letters, 17(9), 2010, 807–810.
  16. [16] B. Babenko, M. Yang, and S. Belongie, Robust object tracking with online multiple instance learning, IEEE Transactions on Learning Pattern Analysis and Machine Intelligence, 33(8), 2011, 1619–1632.
  17. [17] M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua, Fast keypoint recognition using random ferns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 2010, 448–461.
  18. [18] Y. Jin and B. Sendhoff, Pareto-based multiobjective machine learning: An overview and case studies, IEEE Transactions on SMC-C, 38(3), 2008, 397–415.
  19. [19] T. Mori, S. Tamura, and S. Kakui, Incremental estimation of project failure risk with naive bayes classifier, in ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2013, 283–286.
  20. [20] S. Lee and H. Yang, “Lightweight generic random ferns for multi-target augmented reality on mobile devices, Electronics Letters, 49(13), June 2013, 800–802.
  21. [21] Q. Yu, T.B. Dinh, and G. Medioni, Online tracking and reacquisition using co-trained generative and discriminative trackers, Lecture Notes in Computer Science v 5303 LNCS, n PART 2, 10th European Conference on computer Vision, Marseille, France, 2008, 678–691.

Important Links:

Go Back