Abstract
This paper presents a novel method of on-line object tracking with the static and motion saliency features extracted from the video frames locally, regionally and globally. When detecting the salient object, the saliency features are effectively combined in Conditional Random Field (CRF). Then Particle Filter is used when tracking the detected object. Like the attention shifting mechanism of human vision, when the object being tracked disappears, our tracking algorithm can change its target to other object automatically even without re-detection. And different from many other existing tracking methods, our algorithm has little dependence on the surface appearance of the object, so it can detect any category of objects as long as they are salient, and the tracking is robust to the change of global illumination and object shape. Experiments on video clips of various objects show the reliable results of our algorithm.
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References
Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to Detect A Salient Object. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2007)
Liu, T., Zheng, N.N., Ding, W., Yuan, Z.J.: Video Attention: Learning to Detect A Salient Object Sequence. In: 19th International Conference on Pattern Recognition (2008)
Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Rasmussen, C., Hager, G.D.: Probabilistic Data Association Methods for Tracking Complex Visual Objects. IEEE Trans. Pattern Analysis Machine Intell. 23(6), 560–576 (2001)
Hager, G.D., Hager, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Analysis Machine Intell. 20(10), 1025–1039 (1998)
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Proc. European Conf. on Computer Vision, vol. 1, pp. 343–356 (1996)
Leymarie, F., Levine, M.: Tracking deformable objects in the plane using an active contour model. IEEE Trans. Pattern Analysis Machine Intell. 15(6), 617–634 (1993)
Carmi, R., Itti, L.: Visual causes versus correlates of attentional selection in dynamic scenes. Vision Research 46(26), 4333–4345 (2006)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Analysis Machine Intell. 20(11), 1254–1259 (1998)
Felzenszwalb, P.F., Huttenlocher, D.F.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Smith, S.M., Brady, J.M.: ASSET-2: Real-Time Motion Segmentation and Shape Tracking. IEEE Trans. Pattern Analysis Machine Intell. 17(8), 814–820 (1995)
Bouguet, J.Y.: Pyramidal Implementation of the Lucas-Kanade Feature Tracker. Tech. Rep., Intel Corporation, Microprocessor Research Labs (1999)
Collins, R.T., Liu, Y.: On-Line Selection of Discriminative Tracking Features. In: Proc. IEEE Conf. on Computer Vision (2003)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proc. IEEE Conf. on Computer Vision (2001)
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Zhang, G., Yuan, Z., Zheng, N., Sheng, X., Liu, T. (2010). Visual Saliency Based Object Tracking. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_19
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DOI: https://doi.org/10.1007/978-3-642-12304-7_19
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