Skip to main content
SpringerLink
Log in

A silhouette based novel algorithm for object detection and tracking using information fusion of video frames

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Object detection and tracking has been gaining widespread interest and significance with rate of increase in technology towards development of new gadgets. From a continuous video locating a particular object and tracking it is a sequence of process which involves segmentation, preprocessing, extracting the features, finally clustering for recognizing the particular object. This research works highlights the maximum capability of results in order to detect and track the object using a set of algorithms for detection mixed along with the optimization algorithms for better computation time and minimum of errors. The proposed work exploits the contour extraction followed by computation of dissimilarity measure between two signals. A single video sequence partitioned into frames has been optimized for an efficient tracking as evident from the end results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Qi, Y., Soh, C.B., Gunawan, E., Low, K.S., Thomas, R.: Assessment of foot trajectory for object gait phase detection using wireless ultrasonic sensor network. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 88–97 (2016)

    Article  Google Scholar 

  2. Bejarano, N.C., Ambrosini, E., Pedrocchi, A., Ferrigno, G., Monticone, M., Ferrante, S.: A novel adaptive real-time algorithm to detect gait events from wearable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 23(3), 413–422 (2015)

    Article  Google Scholar 

  3. Panahandeh, G., Mohammadiha, N., Leijon, A., Handel, P.: Continuous hidden Markov model for pedestrian activity classification and gait analysis. IEEE Trans. Instrum. Meas. 62(5), 1073–1083 (2013)

    Article  Google Scholar 

  4. Chung, P.-C., Hsu, Y.-L., Wang, C.-Y., Lin, C.-W., Wang, J.-S., Pai, M.-C.: Gait analysis for patients with Alzheimer’s disease using a triaxial accelerometer. In: Proceedings of IEEE International Symposium Circuits Systems, pp. 1323–1326 (2012)

  5. Lai, Z., Xu, Y., Chen, Q., Yang, J., Zhang, D.: Multilinear sparse principal component analysis. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1942–1950 (2014)

    Article  Google Scholar 

  6. Rampp, A., Barth, J., Schülein, S., Gaßmann, K.-G., Klucken, J., Eskofier, B.M.: Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans. Biomed. Eng. 62(4), 1089–1097 (2015)

    Article  Google Scholar 

  7. Iwama, H., Okumura, M., Makihara, Y., et al.: The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans. Inf. Forensics Sec. 7(5), 1511–1521 (2012)

    Article  Google Scholar 

  8. Demonceau, M., et al.: Contribution of a trunk accelerometer system to the characterization of gait in patients with mild-to-moderate Parkinson’s disease. IEEE J. Biomed. Health Inform. 19(6), 1803–1808 (2015)

    Article  Google Scholar 

  9. Weiss, M.J., Moran, M.F., Parker, M.E., Foley, J.T.: Gait analysis of teenagers and young adults diagnosed with autism and severe verbal communication disorders. Front. Integr. Neurosci. 7, 33 (2013)

    Article  Google Scholar 

  10. Pasluosta, C.F., Barth, J., Gassner, H., Klucken, J., Eskofier, B.M.: Pull test estimation in Parkinson’s disease patients using wearable sensor technology. In: Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3109–3112 (2015)

  11. Qi, Y., Soh, C.B., Gunawan, E., Low, K.S., Maskooki, A.: A novel approach to joint flexion/extension angles measurement based on wearable UWB radios. IEEE J. Biomed. Health Inform. 18(1), 300–308 (2014)

    Article  Google Scholar 

  12. Sharma, A., Singh, A., Rohilla, R.: Color based object detection and tracking algorithm using a non-Gaussian adaptive Particle filter. In: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT) (2016)

  13. AzevedoCoste, C., Sijobert, B., Pissard-Gibollet, R., Pasquier, M., Espiau, B., Geny, C.: Detection of freezing of gait in Parkinson disease: preliminary results. Sensors 14(4), 6819–6827 (2014)

    Article  Google Scholar 

  14. Yu, M., Yu, Y., Rhuma, A., Naqvi, S., Wang, L., Chambers, J.: An online one class support vector machine based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE J. Biomed. Health Inform. 17(6), 1002–1014 (2013)

    Article  Google Scholar 

  15. Karg, M., Seiberl, W., Kreuzpointner, F., Haas, J., Kulic, D.: Clinical gait analysis: comparing explicit state duration HMMs using a reference-based index. IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 319–331 (2015)

    Article  Google Scholar 

  16. Yoneyama, M., Kurihara, Y., Watanabe, K., Mitoma, H.: Accelerometry-based gait analysis and its application to Parkinson’s disease assessment—Part 2: a new measure for quantifying walking behavior. IEEE Trans. Neural Syst. Rehabil. Eng. 21(6), 999–1005 (2013)

    Article  Google Scholar 

  17. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  18. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. arXiv preprint arXiv:1612.03144 (2016)

  19. Ren, S., He, K., Girshick, R., Zhang, X., Sun, J.: Object detection networks on convolutional feature maps. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1476–1481 (2017)

    Article  Google Scholar 

Download references

Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 61402544, 61671484, 61702563).

Author information

Authors and Affiliations

  1. College of Electronics and Information Engineering, Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan, China

    Xiaoping Jiang, Jing Sun, Hao Ding & Chenghua Li

Authors
  1. Xiaoping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenghua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, X., Sun, J., Ding, H. et al. A silhouette based novel algorithm for object detection and tracking using information fusion of video frames. Cluster Comput 22 (Suppl 1), 391–398 (2019). https://doi.org/10.1007/s10586-018-2108-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2108-0

Keywords

Search

Navigation