Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Vehicle detection and tracking in airborne videos by multi-motion layer analysis

  • 581 Accesses

  • 28 Citations

Abstract

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

References

  1. 1

    Hu W., Tan T., Wang L., Maybank S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Mans Cybern. Part C Appl. Rev. 34(3), 334–352 (2004)

  2. 2

    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), Article 13 (2006)

  3. 3

    Veeraraghavan H., Masoud O., Papanikolopoulos N.: Computer vision algorithms for intersection monitoring. IEEE Trans. Intell. Transp. Syst. 4(2), 78–89 (2003)

  4. 4

    del-Blanco, C.R., Garcıa, N., Salgado, L., Jaureguizar, F.: Object tracking from unstabilized object tracking from unstabilized embedded camera ego motion. In: International Conference on advanced video and signal based surveillance, pp. 400–405. Genova, Italy (2009)

  5. 5

    Coifman B., Beymer D., McLauchlan P., Malik J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. Part C 6(4), 271–288 (1998)

  6. 6

    Ferryman, J., Maybank, S., Worrall, A.: Visual surveillance for moving vehicles. In: Proceedings, IEEE Workshop on Visual surveillance, pp 73–80. Bombay, India (1998)

  7. 7

    Ali, S., Reilly, V., Shah, M.: Motion and appearance contexts for tracking and re-acquiring targets in aerial videos. In: IEEE conference on computer vision and pattern recognition, pp. 1–6. Minneapolis, MN (2007)

  8. 8

    Beymer, D., McLauchlan, P., Coifman, B., Malik, J.: A real time computer vision system for measuring traffic parameters. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 495–501 (1997)

  9. 9

    Liu, M., Wu, C., Zhang, Y.: A review of traffic visual tracking technology. In: International conference on audio, language and image processing, pp. 1016–1020. Shanghai, China (2008)

  10. 10

    Barron J., Fleet D., Beauchemin S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 42–77 (1994)

  11. 11

    Tian T., Shah M.: Motion estimation and segmentation. Mach. Vis. Appl. 9(1), 32–42 (1996)

  12. 12

    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE conference on computer vision and pattern recognition, pp. 246–252. Fort Collins, CO (1999)

  13. 13

    Wu, C., Cao, X., Lin, R., Wang, F.: Registration based moving vehicle detection for low-altitude urban traffic surveillance. In: Proceedings of the 8th World Congress on intelligent control and automation, pp. 373–378. Jinan, China (2010)

  14. 14

    Lipton, A., Fujiyoshi, H., Patil, R.: Moving target classification and tracking from real time video. In: Proceedings of IEEE workshop applications of computer vision, pp. 8–14 (1998)

  15. 15

    Lin, R., Cao, X., Xu, Y., Wu, C.H.Q.: Airborne moving vehicle detection for video surveillance of urban traffic. IEEE conference on intelligent vehicles symposium, pp. 203–208. Xi’an, China (2009)

  16. 16

    Yu, Q., Medioni, G.: Motion pattern interpretation and detection for tracking moving vehicles in airborne video. In: IEEE conference on computer vision and pattern recognition, pp. 2671–2678. Miami, FL (2009)

  17. 17

    Li, Q., Lei, B., Yu, Y., Hou, R.: Real-time highway traffic information extraction based on airborne video. In: Proceedings of 12th international IEEE conference on intelligent transportation systems, pp. 214–219. St. Louis, MO, USA (2009)

  18. 18

    Huang, Y., Luo, X.: Simultaneous detection and tracking in airborne video. In: International conference on computer technology and development, pp. 320–324. Kota, Kinabalu (2009)

  19. 19

    Kim, Z.W., Malik, J.: Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. In: Proceedings of international conference on computer vision, pp. 521–528. Nice, France (2003)

  20. 20

    Arambel, P., Antone, M., Rago, C., Landau, H., Strat, T.: A multiple-hypothesis tracking of multiple ground targets from aerial video with dynamic sensor control. SPIE (2004)

  21. 21

    JPL, Traffic surveillance and detection technology development. Sensor Development Final Rep., Jet Propulsion Laboratory Publication no. 97–10 (1997)

  22. 22

    Malik, J., Russell, S., Weber, J., Huang, T., Koller, D.: A machine vision based surveillance system for California roads. University of California, PATH project MOU-83 Final Rep. (1994)

  23. 23

    Tao H., Sawhney H., Kumar R.: Object tracking with Bayesian estimation of dynamic layer representations. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 75–89 (2002)

  24. 24

    Ali, S., Shah, M.: A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE conference on computer vision and pattern recognition, pp. 1–8. Minneapolis, MN (2007)

  25. 25

    Hu, M., Ali, S., Shah, M.: Detecting global motion patterns in complex videos. In: International conference on pattern recognition, pp. 1–5. Tampa, FL (2008)

  26. 26

    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: European conference on computer vision (2008)

  27. 27

    Pundlik S., Birchfield S.: Real time motion segmentation of sparse feature points at any speed. IEEE Trans. Syst. Man Cybern. Part B 38(3), 731–742 (2008)

  28. 28

    Fu H., Cao Z., Cao X.: Embedded omni-vision navigator based on multi-object tracking. Mach. Vis. Appl. 22(2), 349–358 (2011)

  29. 29

    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey vision conference, pp. 147–152 (1988)

  30. 30

    Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

  31. 31

    Shi, J., Tomasi, C.: Good features to track. In: IEEE conference on computer vision and pattern recognition, pp. 593–600. Seattle, WA (1994)

  32. 32

    Zitova B., Flusser J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)

  33. 33

    Xiao J., Shah M.: Layer-based video registration. Mach. Vis. Appl. 16(2), 75–84 (2005)

  34. 34

    Cao X., Wu L., Rasheed Z., Liu H., Choe T., Guo F., Haering N.: Automatic geo-registration for port surveillance. Int. J. Patt. Recognit. Artif. Intell. 24(4), 531–555 (2010)

  35. 35

    Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. ICCV. pp. 105–112 (2001)

  36. 36

    Birchfield, S.: Blepo Computer Vision Library. Available at http://www.ces.clemson.edu/~stb/blepo/

  37. 37

    Lin, R., Cao, X., Xu, Y.: Airborne moving vehicle detection for urban traffic surveillance. In: Proceedings of the 11th IEEE conference on intelligent transportation systems, pp. 163–167, Oct. 2008. Beijing

  38. 38

    Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, London (2009) (ISBN 978-0-470-51706-2)

  39. 39

    Isard M., Blake A.: Condensation—Conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

  40. 40

    Cheng Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Patt. Anal. Mach. Intell. 17(8), 790–799 (1995)

Download references

Author information

Correspondence to Pingkun Yan.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Cao, X., Lan, J., Yan, P. et al. Vehicle detection and tracking in airborne videos by multi-motion layer analysis. Machine Vision and Applications 23, 921–935 (2012). https://doi.org/10.1007/s00138-011-0336-x

Download citation

Keywords

  • Vehicle detection
  • Frame registration
  • Feature tracking
  • Partial occlusion
  • Motion layer