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Small dim object tracking using a multi objective particle swarm optimisation technique

Small dim object tracking using a multi objective particle swarm optimisation technique

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Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple hypotheses testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. This study presents a hierarchal tracking system in two levels to solve this problem. For each point in the lower-level, a multi objective particle swarm optimisation technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. The experimental results show that the proposed method has a superior performance in relation to some common dim object tracking methods over different image sequence data sets.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 4. Nemati, A., Kumar, M.: ‘Modeling and control of a single axis tilting quad copter’. American Control Conf. (ACC), Portland, OR, June 2014, pp. 30773082.
    5. 5)
      • 5. Tang, J., Xin, G., Gang, J.: ‘Dim and weak target detection technology based on multi-characteristic fusion’. Proc. 26th Conf. of Spacecraft TT&C Technology, Beijing, China, 2013, pp. 271277.
    6. 6)
      • 6. New, W.L., Tan, M.J., Meng, H.E., Venkateswarlu, R.: ‘New method for detection of dim point targets in infrared images’. SPIE's Int. Symp. on Optical Science, Engineering, and Instrumentation, Denver, CO., July 1999, pp. 141150.
    7. 7)
      • 7. Deshpande, S.D., Meng, H.E., Venkateswarlu, R., Chan, P.: ‘Max-mean and max-median filters for detection of small-targets’. SPIE's Int. Symp. on Optical Science, Engineering, and Instrumentation, Denver, CO., July 1999, pp. 7483.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 11. Hadzagic, M., Michalska, H., Lefebvre, E.: ‘Track-before detect methods in tracking low-observable targets: a survey’, Sensors Trans. Mag., 2005, 54, (1), pp. 374380.
    12. 12)
      • 12. Bariş, C.: ‘Dim target detection in infrared imagery’. PhD Thesis, Middle East Technical University, 2006.
    13. 13)
    14. 14)
    15. 15)
      • 15. Baojun, Z.: ‘Detecting and tracking of multiple targets in IR image sequences in heavy background’. IEEEProc. of CIE Int. Conf. on Radar, 2001, pp. 11411143.
    16. 16)
      • 16. ‘MIT Open Course Ware’, http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-011-introduction-to-communication-control-and-signal-processing-spring-2010, Accessed 9 Jun, 2014, (Oppenheim, A., Verghese, G.: ‘6.011 Introduction to Communication, Control, and Signal Processing, Spring 2010’).
    17. 17)
      • 17. Chung, P.J., Böhme, J.F., Hero, A.O., Mecklenbrauker, C.F.: ‘Detection of the number of signals using a multiple hypothesis test’. Sensor Array and Multichannel Signal Processing Workshop Proc. IEEE, Ann Arbor, MI, July 2004, pp. 221224.
    18. 18)
    19. 19)
    20. 20)
      • 20. Kennedy, J.: ‘The particle swarm: social adaptation of knowledge’. IEEE Int. Conf. on Evolutionary Computation, Indianapolis, IN., April 1997, pp. 303308.
    21. 21)
      • 21. Parsopoulos, K.E., Vrahatis, M.N.: ‘Particle swarm optimization method in multi objective problems’. Proc. of the 2002 ACM Symp. on Applied computing, Madrid, Spain, 2002, pp. 603607.
    22. 22)
    23. 23)
    24. 24)
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