A Hybrid Moving Object Detection Method for Aerial Images

  • Chung-Hsien Huang
  • Yi-Ta Wu
  • Jau-Hong Kao
  • Ming-Yu Shih
  • Cheng-Chuan Chou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


Compared to stationary surveillance cameras, moving object detection on the camera carried by airborne vehicle is more difficult because of the relatively dynamic background. In this study, we present a hybrid method for moving object detection in aerial videos. We compensated the ego motion of airborne vehicle by feature-point based image alignment on consecutive frames, and then applied an accumulative frame differencing method to detect the pixels with motion. Meanwhile, the current frame was divided into homogenous regions by image segmentation, and some of them were selected as candidates of moving objects by prior rules. The motion pixels and the moving object candidates were then fused by a morphing-based approach, obtaining position and shape of moving objects. Moreover, a Kalman-filter tracker was adopted to not only give a consistent label on each detected moving object and but also reject false alarms. The proposed method was evaluated on the videos captured on an airborne vehicle at different altitude. Experimental results revealed that the proposed hybrid method has better performance than both frame-difference and optical-flow based approaches.


moving object detection aerial image video surveillance tracking image segmentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Saleemi, I., Shafique, K., Shah, M.: Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(8), 1472–1485 (2009)CrossRefGoogle Scholar
  2. 2.
    Hsien, J.-W., Hsu, Y.-T., Liao, H.-Y., Chen, C.-C.: Video-based human movement analysis and its application to surveillance systems. IEEE Transactions on Multimedia 10(3), 372–384 (2008)CrossRefGoogle Scholar
  3. 3.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Vision Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  4. 4.
    Desa, S.M., Salih, Q.A.: Image subtraction for real time moving object extraction. In: Proceedings of the International Conference on Computer Graphics. Imaging and Visualization, pp. 41–45 (2004)Google Scholar
  5. 5.
    Cohen, I., Medioni, G.: Detecting and tracking moving objects in video from an airborne observer. In: Proceedings of Image Understanding Workshop, pp. 217–222 (1998)Google Scholar
  6. 6.
    Cohen, I., Medioni, G.: Detecting and tracking moving objects for video surveillance. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2319–2325 (1999)Google Scholar
  7. 7.
    Arambel, P.O., Silver, J., Antone, M., Strat, T.: Signature-aided air-to-ground video tracking. In: Proceedings of International Conference on Information Fusion, pp. 1–8 (2006)Google Scholar
  8. 8.
    Ali, S., Shah, M.: COCOA - Tracking in aerial imagery. In: Proceedings of the SPIE, vol. 6209, p. 62090D (2006)Google Scholar
  9. 9.
    Ali, S., Reilly, V., Shah, M.: Motion and appearance contexts for tracking and re-acquiring targets in aerial videos. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007)Google Scholar
  10. 10.
    Mauther, T., Donoser, M., Bischof, H.: Robust tracking and spatial related components. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1–4 (2008)Google Scholar
  11. 11.
    Brown, A.P., Sullivan, K.J., Miller, D.J.: Feature-aided multiple target tracking in the image plane. In: Proceedings of the SPIE, vol. 6229, p. 62290Q (2006)Google Scholar
  12. 12.
    Xiao, J., Yang, C., Han, F., Cheng, H.: Vehicle and person tracking in UAV videos. In: Proceedings of Multimodal Technologies for Perception of Humans: International Evaluation Workshops, pp. 215–220 (2008)Google Scholar
  13. 13.
    Chung, Y.-C., He, Z.: Low-complexity and reliable moving objects detection and tracking for aerial video Surveillance with small UAVS. In: IEEE International Symposium on Circuits and Systems, pp. 2670–2673 (2007)Google Scholar
  14. 14.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82(1), 35–45 (1960)Google Scholar
  15. 15.
    Wu, Y.-T., Shih, M.-Y., Huang, C.-H.: A Pixel’s Region-Feature Based Image Segmentation and Labeling Method. USA Patent Submitted (2008)Google Scholar
  16. 16.
    Dikmen, M., Ning, H., Lin, D., Cao, L., Le, V., Tsai, S.-F., et al.: Surveillance Event Detection. TRECVID (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chung-Hsien Huang
    • 1
  • Yi-Ta Wu
    • 1
  • Jau-Hong Kao
    • 1
  • Ming-Yu Shih
    • 1
  • Cheng-Chuan Chou
    • 1
  1. 1.Information and Communications Research LaboratoriesIndustrial Technology Research InstituteTaiwan

Personalised recommendations