iccv iccv2013 iccv2013-320 iccv2013-320-reference knowledge-graph by maker-knowledge-mining

320 iccv-2013-Pose-Configurable Generic Tracking of Elongated Objects


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Author: Daniel Wesierski, Patrick Horain

Abstract: Elongated objects have various shapes and can shift, rotate, change scale, and be rigid or deform by flexing, articulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging. We describe a unified, configurable framework for tracking the pose of elongated objects, which move in the image plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of multiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances. While the trend in tracking is to design complex, structure-free algorithms that update object appearance on- line, we show that our tracker, with the novel but remarkably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art performance. Most importantly, our model can be easily configured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.


reference text

[1] A. Adam, E. Rivlin, and I. Shimshoni. Robust fragmentsbased tracking using the integral histogram. In Computer Vision and Pattern Recognition, pages 798–805, 2006. 2, 6, 7

[2] A. Amini, S. Tehrani, and T. Weymouth. Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints. In International Conference

[3]

[4]

[5]

[6] on Computer Vision, pages 95–99, 1988. 3 M. Andriluka, S. Roth, and B. Schiele. Pictorial structures revisited: People detection and articulated pose estimation. In Computer Vision and Pattern Recognition, pages 1014– 1021, 2009. 2 B. Babenko, M. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33:1619–1632, 2011. 2 B. Babenko, M. Yang, and S. J. Belongie. Visual tracking with online Multiple Instance Learning. In Computer Vision and Pattern Recognition, pages 983–990, 2009. 1, 5, 7 L. Cehovin, M. Kristan, and A. Leonardis. An adaptive coupled-layer visual model for robust visual tracking. In International Conference on Computer Vision, pages 1363– 1370, 2011. 1, 2 MethodBrdBSoxequencLeem.Liq.Avg Intersection-over-union[%] [34] Table 1. Performance of our configurable algorithm, evaluated on PROST database. For overall comparison, we also provide average scores in the rightmost column. Best results for each measure are indicated in bold.

[7] R. Collins. Mean-shift blob tracking through scale space. In Computer Vision and Pattern Recognition, pages 234–240, 2003. 2

[8] D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Computer Vision and Pattern Recognition, 2000. 2 2926

[9] Z. Fan, M. Yang, and Y. Wu. Multiple collaborative kernel tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7):1268–1273, 2007. 2

[10] P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. International Journal of Computer Vision, 61(1):55–79, 2005. 2, 4

[11] M. Fischler and R. Elschlager. The representation and matching of pictorial structures. IEEE Transactions on Computers, C-22(1):67 92, 1973. 2 M. Godec, P. Roth, and H. Bischof. Hough-based tracking of non-rigid objects. In International Conference on Computer Vision, pages 81–88, 2011. 1, 2 S. Gu, Y. Zheng, and C. Tomasi. Efficient visual object tracking with online nearest neighbor classifier. In Asian Conference on Computer Vision, pages 271–282, 2010. 7 G. Hager, M. Dewan, and C. Stewart. Multiple kernel tracking with SSD. In Computer Vision and Pattern Recognition, pages 790–797, 2004. 2 T. H. Heibel, B. Glocker, M. Groher, N. Paragios, N. Komodakis, and N. Navab. Discrete tracking of parametrized curves. In Computer Vision and Pattern Recognition, pages 1754–1761, 2009. 3, 5 M. Isard and A. Blake. CONDENSATION - conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998. 2 H. Jiang, T.-P. Tian, K. He, and S. Sclaroff. Scale resilient, rotation invariant articulated object matching. In Computer Vision and Pattern Recognition, pages 143–150. IEEE, 2012. 2, 3 W. Kabsch. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, 34(5):827–828, 1978. 4 Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learningdetection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7): 1409–1422, 2012. 1, 6, 7 L. Karlinsky, M. Dinerstein, D. Harari, and S. Ullman. The chains model for detecting parts by their context. In Computer Vision and Pattern Recognition, pages 25–32, 2010. 3 –

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21] M. Kass, A. Witkin, and D. Terzopoulos. Snakes - active contour models. International Journal Of Computer Vision, 1(4):321–331, 1987. 3

[22] D. Klein and A. Cremers. Boosting scalable gradient features for adaptive real-time tracking. In International Conference on Robotics and Automation, pages 4411–4416, 2011. 1, 6, 7

[23] J. Kwon and K. Lee. Tracking of a non-rigid object via patchbased dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In Computer Vision and Pattern Recognition, 2009. 1

[24] I. Leichter. Mean shift trackers with cross-bin metrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4):695–706, 2012. 2

[25] E. Maggio, F. Smeraldi, and A. Cavallaro. Combining colour and orientation for adaptive particle filter-based tracking. In British Machine Vision Conference, 2005. 3, 7

[26] T. Mauthner, M. Donoser, and H. Bischof. Robust tracking of spatial related components. In International Conference on Pattern Recognition, pages 1–4, 2008. 2

[27] N. Padoy and G. Hager. Deformable tracking of textured curvilinear objects. In British Machine Vision Conference, 2012. 5

[28] D. Park and D. Ramanan. N-best maximal decoders for part models. In International Conference on Computer Vision, pages 2627–2634, 2011. 1

[29] P. P ´erez, C. Hue, J. Vermaak, and M. Gangnet. Color-based probabilistic tracking. In European Conference on Computer Vision, volume 2350, pages 661–675, 2002. 2

[30] F. M. Porikli. Integral histogram: A fast way to extract histograms in cartesian spaces. In Computer Vision and Pattern Recognition, pages 829–836, 2005. 5 [3 1] D. Ramanan, D. A. Forsyth, and A. Zisserman. Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1):65–8 1, 2007. 2, 4

[32] R. Ronfard. Region-based strategies for active contour models. International Journal of Computer Vision, 13(2):229– 251, 1994. 3

[33] A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof. On-line random forests. In International Conference on Computer Vision Workshop on On-line Learning for Computer Vision, 2009. 1, 7

[34] J. Santner, C. Leistner, A. Saffari, T. Pock, and H. Bischof. PROST: Parallel robust online simple tracking. In Computer Vision and Pattern Recognition, pages 723–730, 2010. 1, 5, 7

[35] B. Sapp, D. Weiss, and B. Taskar. Parsing human motion with stretchable models. In Computer Vision and Pattern Recognition, 2011. 3

[36] J. Saragih, S. Lucey, and J. Cohn. Deformable model fitting by regularized landmark mean-shift. International Journal of Computer Vision, 91(2):200–215, 2011. 1

[37] S. Sclaroff and J. Isidoro. Active blobs: region-based, deformable appearance models. Computer Vision and Image Understanding, 89(2-3): 197–225, 2003. 3

[38] S. M. N. Shahed, J. Ho, and M. Yang. Online visual tracking with histograms and articulating blocks. Computer Vision and Image Understanding, 114(8):901–914, 2010. 2

[39] C. Shen, M. J. Brooks, and A. van den Hengel. Fast global kernel density mode seeking with application to localisation and tracking. In International Conference on Computer Vision, pages 15 16–1523, 2005. 2

[40] D. Wesierski, P. Horain, and Z. Kowalczuk. EBE: Elastic Blob Ensemble for coarse human tracking. In International Conference on Image Processing, pages 1625–1628, 2012. 3

[41] K. Wnuk and S. Soatto. Multiple instance filtering. In Neural Information Processing Systems, pages 370–378, 2011. 1, 7

[42] Y. Yang and D. Ramanan. Articulated pose estimation using flexible mixtures of parts. In Computer Vision and Pattern Recognition, 2011. 2, 4

[43] A. Yilmaz. Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In Computer Vision and Pattern Recognition, 2007. 2, 4

[44] X. Zhu and D. Ramanan. Face detection, pose estimation, and landmark localization in the wild. In Computer Vision and Pattern Recognition, 2012. 2 2927