Abstract
We propose a region-based body joint tracking scheme to track and estimate continuous joint locations in low resolution imagery where the estimated trajectories can be analyzed for specific gait signatures. The true transition between the joint states are of a continuous nature and specifically follows a sinusoidal trajectory. Recent state of art techniques enables us to estimate pose at each frame from which joint locations can be deduced. But these pose estimates at low resolution are often noisy and discrete and hence not suitable for further gait analysis. Our proposed 2-level region-based tracking scheme gets a good approximation to the true trajectory and obtains finer estimates. Initial joint locations are deduced from a human pose estimation algorithm and subsequent finer locations are estimated and tracked by a Kalman filter. We test the algorithm on sequences containing individuals walking outdoors and evaluate their gait using the estimated joint trajectories.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bagdanov, A., Del Bimbo, A., Dini, F., Lisanti, G., Masi, I.: Posterity logging of face imagery for video surveillance. IEEE MultiMedia 19(4), 48–59 (2012)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The clear mot metrics. J. Image Video Process, 1:1–1:10 (January 2008)
Burgos-Artizzu, X., Hall, D., Perona, P., Dollar, P.: Merging pose estimates across space and time. In: Proceedings of the British Machine Vision Conference. BMVA Press (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)
Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (June 2008)
Forstner, W., Moonen, B.: A metric for covariance matrices (1999)
Huang, C.H., Boyer, E., Ilic, S.: Robust human body shape and pose tracking. In: 2013 International Conference on 3DV-Conference, pp. 287–294 (2013)
Kaaniche, M., Bremond, F.: Tracking hog descriptors for gesture recognition. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 140–145 (2009)
Kohler, M.: Using the Kalman Filter to Track Human Interactive Motion: Modelling and Initialization of the Kalman Filter for Translational Motion. Forschungsberichte des Fachbereichs Informatik der Universität Dortmund, Dekanat Informatik, Univ. (1997)
Nair, B.M., Kendricks, K.D., Asari, V.K., Tuttle, R.F.: Optical flow based kalman filter for body joint prediction and tracking using hog-lbp matching, vol. 9026, pp. 90260H–90260H–14 (2014)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ramakrishna, V., Kanade, T., Sheikh, Y.: Tracking human pose by tracking symmetric parts. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3728–3735 (2013)
Ramanan, D.: Learning to parse images of articulated bodies. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 1129–1136. MIT Press (2007)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1297–1304 (2011)
Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1385–1392 (June 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nair, B.M., Kendricks, K.D., Asari, V.K., Tuttle, R.F. (2014). Body Joint Tracking in Low Resolution Video Using Region-Based Filtering. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-14249-4_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14248-7
Online ISBN: 978-3-319-14249-4
eBook Packages: Computer ScienceComputer Science (R0)