Abstract
We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. The algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera’s information contribution, and variable action costs.
We evaluate our method by comparing it to previous approaches with a prerecorded sequence of real world images.
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Paletta, L., Pinz, A.: Active object recognition by view integration and reinforcement learning. Robotics and Autonomous Systems 31(1-2), 71–86 (2000)
Denzler, J., Brown, C.: Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 145–157 (2002)
Deinzer, F., Denzler, J., Niemann, H.: Viewpoint Selection – Planning Optimal Sequences of Views for Object Recognition. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 65–73. Springer, Heidelberg (2003)
Fayman, J., Sudarsky, O., Rivlin, E., Rudzsky, M.: Zoom tracking and its applications. Machine Vision and Applications 13, 25–37 (2001)
Tordoff, B., Murray, D.: Reactive zoom control while tracking using an affine camera. In: Proc. 12th British Machine Vision Conference, September 2001, vol. 1, pp. 53–62 (2001)
Micheloni, C., Foresti, G.L.: Zoom on Target While Tracking. In: Proceedings of the International Conference on Image Processing, Genua, Italy, vol. 3, pp. 117–120 (2005)
Kalandros, M.K., Pao, L.Y., Ho, Y.: Randomization and super-heuristics in choosing sensor sets in target tracking applications. In: Proc. IEEE Conf. Decision and Control, Phoenix, AZ, pp. 1803–1808 (1999)
Denzler, J., Zobel, M., Niemann, H.: Information Theoretic Focal Length Selection for Real-Time Active 3-D Object Tracking. In: International Conference on Computer Vision, Nice, France, pp. 400–407 (2003)
Deutsch, B., Zobel, M., Denzler, J., Niemann, H.: Multi-step entropy based sensor control for visual object tracking. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 359–366. Springer, Heidelberg (2004)
Deutsch, B., Deinzer, F., Zobel, M., Denzler, J.: Multi-Step Active Object Tracking with Entropy Based Optimal Actions Using the Sequential Kalman Filter. In: Araújo, H., Vieira, A., Braz, J., Encarnação, B., Carvalho, M. (eds.) Proceedings of the International Conference on Image Processing, Setúbal, Portugal, vol. 2, pp. 169–176. INSTICC Press, Setúbal (2005)
Kalman, R.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 35–44 (1960)
Chui, C.K., Chen, G.: Kalman Filtering. Springer, Heidelberg (1991)
Bar-Shalom, Y., Fortmann, T.: Tracking and Data Association. Academic Press, Boston (1988)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Törn, A., Žilinskas, A.: Global Optimization. LNCS, vol. 350. Springer, Heidelberg (1989)
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Deutsch, B., Wenhardt, S., Niemann, H. (2006). Multi-step Multi-camera View Planning for Real-Time Visual Object Tracking. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_54
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DOI: https://doi.org/10.1007/11861898_54
Publisher Name: Springer, Berlin, Heidelberg
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