Silhouette-Based Real-Time Object Detection and Tracking

  • Bhaumik Vaidya
  • Harendra Panchal
  • Chirag Paunwala
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)


Object detection and tracking in the video sequence is a challenging task and time-consuming process. Intrinsic factors like pose, appearance, variation in scale and extrinsic factors like variation in illumination, occlusion, and clutter are major challenges in object detection and tracking. The main objective of the tracking algorithm is accuracy and speed in each frame. We propose the best combination of detection and tracking algorithm which performs very efficiently in real time. In proposed algorithm, object detection task is performed from given sketch using Fast Directional Chamfer Matching (FDCM) which is capable of handling some amount of deformation in edges. To deal with the articulation condition, part decomposition algorithm is used in the proposed algorithm. Combination of these two parts is capable enough to handle deformation in shape automatically. Amount of time taken to perform this algorithm depends on the size and edge segment in the input frame. For object tracking, Speeded up Robust Features (SURF) algorithm is used because of its rotation invariant and fast performance features. The proposed algorithm works in all situations without the prior knowledge about number of frames.


Convexity defects Fast directional chamfer matching Part decomposition Speeded up robust features 


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Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.


  1. 1.
    Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review, 11, pp. 31–66 (2014).Google Scholar
  2. 2.
    Bhattacharjee, S. D., Mittal, A.: Part-based deformable object detection with a single sketch. Computer Vision and Image Understanding, vol. 139, pp. 73–87 (2015).Google Scholar
  3. 3.
    Bouwmans, T.: Recent advanced statistical background modeling for foreground detection-a systematic survey. Recent Patents on Computer Science, vol. 4 no. 3, pp. 147–176 (2011).Google Scholar
  4. 4.
    Stauffer, C., Grimson, W. E. L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on. Computer Vision and Pattern Recognition, Vol. 2, pp. 246–252, IEEE (1999).Google Scholar
  5. 5.
    Lin, H. H., Liu, T. L., & Chuang, J. H.: A probabilistic SVM approach for background scene initialization. In: International Conference on Image Processing Vol. 3, pp. 893–896. IEEE (2002).Google Scholar
  6. 6.
    Wang, J., Bebis, G., Miller, R.: Robust video-based surveillance by integrating target detection with tracking. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW’06, IEEE (2006).Google Scholar
  7. 7.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, Vol. 1, pp. 255–261, IEEE (1999).Google Scholar
  8. 8.
    Ridder, C., Munkelt, O., Kirchner, H.: Adaptive background estimation and foreground detection using kalman-filtering. In: Proceedings of International Conference on recent Advances in Mechatronics, pp. 193–199 (1995).Google Scholar
  9. 9.
    Chang, R., Gandhi, T., & Trivedi, M. M.: Vision modules for a multi-sensory bridge monitoring approach. In: Proceedings of 7th International IEEE Conference on Intelligent Transportation Systems, pp. 971–976, IEEE (2004).Google Scholar
  10. 10.
    Butler, D. E., Bove, V. M., & Sridharan, S.: Real-time adaptive foreground/background segmentation. In: EURASIP Journal on Advances in Signal Processing (2005).Google Scholar
  11. 11.
    Kim, K., Chalidabhongse, T. H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing (ICIP’04), Vol. 5, pp. 3061–3064, IEEE., 2004.Google Scholar
  12. 12.
    Bai, X., Li, Q., Latecki, L. J., Liu, W., Tu, Z.: Shape band: A deformable object detection approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1335–1342, IEEE (2009).Google Scholar
  13. 13.
    Gopalan, R., Turaga, P., Chellappa, R.: Articulation-invariant representation of non-planar shapes. In: Computer Vision–ECCV 2010, 286–299 (2010).Google Scholar
  14. 14.
    Suzuki, S.: Topological structural analysis of digitized binary images by border following. In: Computer vision, graphics, and image processing, vol 30 no. 1, pp. 32–46 (1985).Google Scholar
  15. 15.
    Graham, R. L., Yao, F. F.: Finding the convex hull of a simple polygon. In: Journal of Algorithms, vol 4 no. 4, pp. 324–331 (1983).Google Scholar
  16. 16.
    Youssef, M. M., Asari, V. K.: Human action recognition using hull convexity defect features with multi-modality setups. In: Pattern Recognition Letters, vol. 34 no. 15, pp. 1971–1979 (2013).Google Scholar
  17. 17.
  18. 18.
    Liu, M. Y., Tuzel, O., Veeraraghavan, A., Chellappa, R.: Fast directional chamfer matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1696–1703. IEEE (2010).Google Scholar
  19. 19.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. Speeded-up robust features (SURF). In: Computer vision and image understanding, vol. 110 no. 3, pp. 346–359 (2008).Google Scholar
  20. 20.
    Zunic, J., Rosin, P. L.: A new convexity measure for polygons. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26 no. 7, pp. 923–934 (2004).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Bhaumik Vaidya
    • 1
  • Harendra Panchal
    • 2
  • Chirag Paunwala
    • 2
  1. 1.GTUAhmedabadIndia
  2. 2.EC DepartmentSCETSuratIndia

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