iccv iccv2013 iccv2013-87 iccv2013-87-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Martin Schiegg, Philipp Hanslovsky, Bernhard X. Kausler, Lars Hufnagel, Fred A. Hamprecht
Abstract: The quality of any tracking-by-assignment hinges on the accuracy of the foregoing target detection / segmentation step. In many kinds of images, errors in this first stage are unavoidable. These errors then propagate to, and corrupt, the tracking result. Our main contribution is the first probabilistic graphical model that can explicitly account for over- and undersegmentation errors even when the number of tracking targets is unknown and when they may divide, as in cell cultures. The tracking model we present implements global consistency constraints for the number of targets comprised by each detection and is solved to global optimality on reasonably large 2D+t and 3D+t datasets. In addition, we empirically demonstrate the effectiveness of a postprocessing that allows to establish target identity even across occlusion / undersegmentation. The usefulness and efficiency of this new tracking method is demonstrated on three different and challenging 2D+t and 3D+t datasets from developmental biology.
[1] H. Ben Shitrit, J. Berclaz, F. Fleuret, and P. Fua. Tracking multiple people under global appearance constraints. ICCV, 2011. 3
[2] B. Bose, X. Wang, and E. Grimson. Multi-class object tracking algorithm that handles fragmentation and grouping. In CVPR, 2007. 3
[3] L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001. 4, 6
[4] E. Fox, D. Choi, and A. Willsky. Nonparametric Bayesian methods for large scale multi-target tracking. In ACSSC, 2006. 3
[5] B. X. Kausler, M. Schiegg, B. Andres, M. Lindner, U. Koethe, H. Leitte, J. Wittbrodt, L. Hufnagel, F. A. Ham-
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] precht, and U. K ¨othe. A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. In ECCV, 2012. 3, 6, 7 D. Koller and N. Friedman. Probabilistic Graphical Models, 2010. 3 F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47:498–519, 2001. 3 X. Lou and F. A. Hamprecht. Structured Learning for Cell Tracking. NIPS, 2011. 3 X. Lou, U. Koethe, J. Wittbrodt, and F. A. Hamprecht. Learning to Segment Dense Cell Nuclei with Shape Prior. In CVPR, 2012. 8 E. Meijering, O. Dzyubachyk, and I. Smal. Methods for cell and particle tracking. Methods in Enzymology: Live Cell Imaging, 2012. 1 P. Nillius, J. Sullivan, and S. Carlsson. Multi-target tracking: Linking identities using Bayesian network inference. In CVPR, 2006. 3 D. R. Padfield, J. Rittscher, and B. Roysam. Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Medical Image Analysis, 15(4):650–668, 2011. 3 J. C. Rubio, J. Serrat, A. M. Lopez, and D. Ponsa. Multiple target tracking for intelligent headlights control. 13th International IEEE Conference on Intelligent Transportation Systems, pages 903–910, Sept. 2010. 1 S. Sahni. Computationally related problems. SIAM Journal on Computing, 3(4):262–279, 1974. 2, 3 C. Sommer, C. Straehle, U. Kothe, and F. A. Hamprecht. Ilastik: Interactive learning and segmentation toolkit. ISBI,
[16]
[17]
[18]
[19] (1):230–233, 2011. 6 C. Wang, M. de La Gorce, and N. Paragios. Segmentation, ordering and multi-object tracking using graphical models. In ICCV, 2009. 2 Z. Wu, A. Thangali, S. Sclaroff, and M. Betke. Coupling detection and data association for multiple object tracking. In CVPR, 2012. 2 A. Yilmaz, O. Javed, and M. Shah. Object tracking: A survey. ACM Computing Surveys (CSUR), 38(4), 2006. 1 L. Zhang, Y. Li, and R. Nevatia. Global data association for multi-object tracking using network flows. In CVPR, 2008. 3 2935