Abstract
Visual tracking is the task of estimating the trajectory of an object in a video given its initial location. This is usually done by combining at each step an appearance and a motion model. In this work, we learn from a small set of training trajectory annotations how the objects in the scene typically move. We learn the relative weight between the appearance and the motion model. We call this weight: visual deceptiveness. At test time, we transfer the deceptiveness and the displacement from the closest trajectory annotation to infer the next location of the object. Further, we condition the transference on an event model. On a set of 161 manually annotated test trajectories, we show in our experiments that learning from just 10 trajectory annotations halves the center location error and improves the success rate by about 10%.
Chapter PDF
Similar content being viewed by others
References
Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)
Cifuentes, C.G., Sturzel, M., Jurie, F., Brostow, G.J.: Motion models that only work sometimes. In: BMVC (2012)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. PAMI 27(10), 1631–1643 (2005)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC2012) Results (2012), http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV (2011)
Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. PAMI 34(7), 1409–1422 (2012)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering (1960)
Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. PAMI 27(11), 1805–1918 (2005)
Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 201–214. Springer, Heidelberg (2012)
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. PAMI (2006)
Kuettel, D., Breitenstein, M.D., Van Gool, L., Ferrari, V.: What’s going on? Discovering spatio-temporal dependencies in dynamic scenes. In: CVPR (2010)
Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)
Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.J.: Coupled object detection and tracking from static cameras and moving vehicles. PAMI 30(10), 1683–1698 (2008)
Li, X., Dick, A., Wang, H., Shen, C., van den Hengel, A.: Graph mode-based contextual kernels for robust svm tracking. In: ICCV (2011)
Liu, J., Carr, P., Collins, R.T., Liu, Y.: Tracking sports players with context-conditioned motion models. In: CVPR (2013)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV (2009)
Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: Locally orderless tracking. In: CVPR (2012)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Prosser, P.: Hybrid algorithms for the constraint satisfaction problem. In: Computational Intelligence (1993)
Rodriguez, M., Ali, S., Kanade, T.: Tracking in unstructured crowded scenes. In: ICCV (2009)
Segal, A.V., Reid, I.D.: Latent data association: Bayesian model selection for multi-target tracking. In: ICCV (2013)
Smith, K., Carleton, A., Lepetit, V.: General constraints for batch multiple-target tracking applied to large-scale videomicroscopy. In: CVPR (2008)
Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: A benchmark. In: CVPR (2013)
Yang, B., Nevatia, R.: Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In: CVPR (2012)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. In: ICASSP (2006)
Yuen, J., Russell, B.C., Liu, C., Torralba, A.: Labelme video: Building a video database with human annotations. In: ICCV (2009)
Zhang, T., Ghanem, B., Ahuja, N.: Robust multi-object tracking via cross-domain contextual information for sports video analysis. In: ICASSP (2012)
Zhao, X., Medioni, G.: Robust unsupervised motion pattern inference from video and applications. In: ICCV (2011)
Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors:learning a mixture model of dynamic pedestrian-agents. In: CVPR (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Manen, S., Kwon, J., Guillaumin, M., Van Gool, L. (2014). Appearances Can Be Deceiving: Learning Visual Tracking from Few Trajectory Annotations. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-10602-1_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10601-4
Online ISBN: 978-3-319-10602-1
eBook Packages: Computer ScienceComputer Science (R0)