access icon free Extended social force model-based mean shift for pedestrian tracking under obstacle avoidance

It has been shown that mean shift tracking algorithm can achieve excellent results in pedestrian tracking task. It empirically estimates the target position of current frame by locating the maximum of a density function from the local neighborhood of the target position of previous frame. However, this method only considers its past trajectory without considering the influence of pedestrian environment when applying to pedestrian tracking. In practical, pedestrians always keep a safe distance away from obstacles when programming their paths. To address the issue of obstacle avoidance, this paper proposes a novel extended social force model-based mean shift tracking algorithm in which pedestrian environment is full taken in consideration. Firstly, an extended social force model is presented to quantify the interaction between pedestrian and obstacle by means of force. Furthermore, directional weights and speed weights are introduced to adjust the strength of the force in terms of the difference of individual perspectives and relative velocities. Finally, the initial target position is predicted by Newton's laws of motion and then the Mean Shift method is integrated to track target position. Experiment results show that this algorithm achieves an encouraging performance when obstacles exist.

Inspec keywords: object tracking; pedestrians

Other keywords: individual perspectives; relative velocities; pedestrian environment; target position tracking; local neighbourhood; pedestrian tracking; Newton motion law; force viewpoint; density function; extended social force model-based mean shift tracking algorithm; directional weights; target-obstacle interaction; obstacle avoidance; speed weights

Subjects: Traffic engineering computing; Optical, image and video signal processing; Computer vision and image processing techniques

References

    1. 1)
      • 33. Gao, Y., Luh, P.B., Zhang, H., et al: ‘A modified social force model considering relative velocity of pedestrians’. IEEE Int. Conf. on Automation Science and Engineering, 2013, pp. 747751.
    2. 2)
      • 32. Johansson, A., Helbing, D., Shukla, P.K.: ‘Specification of a microscopic pedestrian model by evolutionary adjustment to video tracking data’, Adv. Complex Syst., 2008, 10, (4), pp. 271288.
    3. 3)
      • 17. Robin, T., Antonini, G., Bierlaire, M., et al: ‘Specification, estimation and validation of a pedestrian walking behavior model’, Transp. Res. B, Methodol., 2009, 43, (1), pp. 3656.
    4. 4)
      • 7. Yan, X., Kakadiaris, I.A., Shah, S.K.: ‘Modeling local behavior for predicting social interactions towards human tracking’, Pattern Recogn., 2014, 47, (4), pp. 16261641.
    5. 5)
      • 28. Mehran, R., Oyama, A., Shah, M.: ‘Abnormal crowd behavior detection using social force model’. Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2009, pp. 935942.
    6. 6)
      • 31. Kim, S., Guy, S.J., Hillesland, K., et al: ‘Velocity-based modeling of physical interactions in dense crowds’, Vis. Comput., 2014, 31, (5), pp.541555doi: 10.1007/s00371-014-0946-1.
    7. 7)
      • 3. Mekonnena, A.A., Leraslea, F., Herbulota, A.: ‘Cooperative passers-by tracking with a mobile robot and external cameras’, Comput. Vis. Image Underst., 2013, 117, (10), pp. 12291244.
    8. 8)
      • 5. Lee, D., Kim, G., Kim, D., et al: ‘Vision-based object detection and tracking for autonomous navigation of underwater robots’, Ocean Eng., 2012, 48, pp. 5968.
    9. 9)
      • 18. Roy, D., Krishnamurthy, A., Heragu, S., et al: ‘Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems’, Eur. J. Oper. Res., 2015, 242, (1), pp. 7287.
    10. 10)
      • 15. Henein, C.M., White, T.: ‘Microscopic information processing and communication in crowd dynamics’, Phys. A, Stat. Mech. Appl., 2010, 389, (21), pp. 46364653.
    11. 11)
      • 19. Helbing, D., Farkas, I., Vicsek, T.: ‘Simulating dynamical features of escape panic’, Nature, 2000, 407, (6803), pp. 487490.
    12. 12)
      • 27. Stoudt, H.W., Damon, A., McFarland, R., et al: ‘Weight, height, and selected body dimensions of adults’. Vital and health statistics series 11 (DHEW Publication (PHS) 8, National Center for Health Statistics, Rockville, MD, 1969).
    13. 13)
      • 13. Saboia, P., Goldenstein, S.: ‘Crowd simulation: applying mobile grids to the social force model’, Vis. Comput., 2012, 28, (10), pp. 10391048.
    14. 14)
      • 16. Kuwano, M., Zhang, J.Y., Fujiwara, A.: ‘Dynamic discrete choice model for multiple social interactions’, Transp. Res. Board, 2011, 2231, pp. 6875.
    15. 15)
      • 24. Li, Y., Liang, S., Bai, B.D., et al: ‘Detecting and tracking dim small targets in infrared image sequences under complex backgrounds’, Multimedia Tools Appl., 2014, 71, (3), pp. 11791199.
    16. 16)
      • 22. Siyit, R., Ding, Y.G.: ‘Research and realization of mean shift tracking algorithm’, J. Comput. Inf. Syst., 2014, 10, (7), pp. 30573064.
    17. 17)
      • 29. Curtis, S., Zafar, B., Gutub, A., et al: ‘Right of way’, Vis. Comput., 2013, 29, (12), pp. 12771292.
    18. 18)
      • 36. Parisi, D.R., Gilman, M., Moldovan, H.: ‘A modification of the social force model can reproduce experimental data of pedestrian flows in normal conditions’, Phys. A, Stat. Mech. Appl., 2009, 338, (17), pp. 36003608.
    19. 19)
      • 38. Kaysi, I., Alshalalfah, B., Shalaby, A., et al: ‘Users’ evaluation of rail systems in mass events: case study in Mecca, Saudi Arabia’, Transp. Res. Rec., J. Transp. Res. Board, 2013, (2350), pp. 111118.
    20. 20)
      • 2. Rao, Y.B.: ‘Automatic vehicle recognition in multiple cameras for video surveillance’, Vis. Comput., 2015, 31, (3), pp. 271280.
    21. 21)
      • 26. Seer, S., Rudloff, C., Matyus, T., et al: ‘Validating social force based models with comprehensive real world motion data’, Transp. Res. Procedia, 2014, 2, pp. 724732.
    22. 22)
      • 23. Maalouf, A., Larabi, M.C., Nicholson, D.: ‘Offline quality monitoring for legal evidence images in video-surveillance applications’, Multimedia Tools Appl., 2014, 73, (1), pp. 189218.
    23. 23)
      • 11. Pellegrini, S., Ess, A., Schindler, K.: ‘You'll never walk alone: modeling social behavior for multi-target tracking’. Proc. of IEEE Int. Conf. on Computer Vision, 2009, pp. 261268.
    24. 24)
      • 21. Bousetouane, F., Dib, L., Snoussi, H.: ‘Improved mean shift integrating texture and color features for robust real time object tracking’, Vis. Comput., 2013, 29, (3), pp. 155170.
    25. 25)
      • 39. Abdelgawad, H., Shalaby, A., Abdulhai, B., et al: ‘Microscopic modeling of large-scale pedestrian–vehicle conflicts in the city of Madinah, Saudi Arabia’, J. Adv. Transp., 2014, 48, (6), pp. 507525.
    26. 26)
      • 25. Johansson, F., Peterson, A., Tapani, A.: ‘Waiting pedestrians in the social force model’, Phys. A, Stat. Mech. Appl., 2015, 419, pp. 95107.
    27. 27)
      • 14. Jian, X.X., Wong, S.C., Zhang, P., et al: ‘Perceived cost potential field cellular automata model with an aggregated force for pedestrian dynamics’, Transp. Res. C, Emerg. Technol., 2014, 42, pp. 200210.
    28. 28)
      • 10. Zheng, Y.Z., Wang, H.Y., Guo, Q.X.: ‘A novel mean shift algorithm combined with least square approach and its application in target tracking’. Proc. of IEEE Int. Conf. on Signal Processing, 2012, pp. 11021105.
    29. 29)
      • 20. Helbing, D., Buzna, L., Johansson, A., et al: ‘Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions’, Transp. Sci., 2005, 39, (1), pp. 124.
    30. 30)
      • 37. Yin, F., Dimitrios, M., Sergio, A.V.: ‘Performance evaluation of object tracking algorithms’. IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance, 2007.
    31. 31)
      • 4. Zhou, W., Fei, Z., Hu, H.S., et al: ‘Real-time object subspace searching based on discrete searching paths and local energy’, Int. J. Autom. Comput., 2016, 13, (2), pp. 99107.
    32. 32)
      • 8. Beyan, C., Temizel, A.: ‘Adaptive mean-shift for automated multi object tracking’, IET Comput. Vis., 2012, 6, (1), pp. 112doi: 10.1049/iet-cvi.2011.0054.
    33. 33)
      • 1. Torabi, A., Massé, G., Bilodeau, G.A.: ‘An iterative integrated framework for thermal–visible image registration, sensor fusion, and people tracking for video surveillance applications’, Comput. Vis. Image Underst., 2012, 116, (2), pp. 210221.
    34. 34)
      • 6. Pai, C.J., Tyan, H.R., Liang, Y.M.: ‘Pedestrian detection and tracking at crossroads’, Pattern Recogn., 2004, 37, (5), pp. 10251034.
    35. 35)
      • 12. Zeng, W.L., Nakamura, H., Chen, P.: ‘A modified social force model for pedestrian behavior simulation at signalized crosswalks’, Procedia, Soc. Behav. Sci., 2014, 138, pp. 521530.
    36. 36)
      • 30. Löhner, R.: ‘On the modeling of pedestrian motion’, Appl. Math. Model., 2010, 34, (2), pp. 366382.
    37. 37)
      • 9. An, X., Kim, J., Han, Y.: ‘Optimal colour-based mean shift algorithm for tracking objects’, IET Comput. Vis., 2012, 8, (3), pp. 235244doi: 10.1049/iet-cvi.2013.0004.
    38. 38)
      • 34. Pellegrini, S., Ess, A., Schindler, K., et al: ‘You'll never walk alone: modeling social behavior for multi-target tracking’. 2009 IEEE 12th Int. Conf. on Computer Vision, September 2009, pp. 261268.
    39. 39)
      • 35. ‘Ethz – computer vision lab: datasets’. Available at http://www.vision.ee.ethz.ch/datasets/index.en.html.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0022
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0022
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading