ABSTRACT
In this paper, we present a single-object long-term tracker that supports high appearance changes in the tracked target, occlusions, and is also capable of recovering a target lost during the tracking process. The initial motivation was real time automatic speaker tracking by a static camera in order to control a PTZ camera capturing a lecture. The algorithm consists of a novel combination of state-of-the-art techniques. Subjective evaluation, over existing and newly recorded sequences, shows that the tracker is able to overcome the problems and difficulties of long-term tracking in a real lecture. Additionally, in order to further assess the performance of the proposed approach, a comparative evaluation over the VOT2013 dataset is presented.
- Y. Rui, A. Gupta, J. Grudin, and L. He, "Automating lecture capture and broadcast: technology and videography," ACM Multimedia Systems Journal, 10:3--15, 2004.Google ScholarDigital Library
- C. Zhang, Y. Rui, J. Crawford, and L. He, "An automated end-toend lecture capturing and broadcasting system," in ACM Multimedia, pp. 808--809, 2005. Google ScholarDigital Library
- C. Zhang, Y. Rui, L. He, and M. Wallick, "Hybrid speaker tracking in an automated lecture room," in International Conference on Multimedia and Expo, pp. 1--4, 2005.Google Scholar
- H.P. Chou, J.M Wang, C.S. Fuh, S.C. Lin, and S.W. Chen, "Automated lecture recording system," in Conference on System Science and Engineering, pp. 167--172, 2010.Google Scholar
- D. Pang, S. Madan, S. Kosaraju, and T. Vir Singh, "Automatic virtual camera view generation for lecture videos," Tech. Rep., Stanford Universit, 2010.Google Scholar
- T. Yokoi and H. Fujiyoshi, "Virtual camerawork for generating lecture video from high resolution images," in Conference on Multimedia and Expo, pp. 1--4, 2005.Google Scholar
- A.W.M. Smeulders, et al., "Visual Tracking: An Experimental Survey", IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7):1442--1468, 2014Google ScholarDigital Library
- P.D.Z. Varcheie and G.A. Bilodeau, "Active people tracking by a ptz camera in ip surveillance system," in Workshop on Robotic and Sensors Environments, pp. 98--103, 2009.Google Scholar
- P.D.Z. Varcheie and G.A. Bilodeau, "Human tracking by ip ptz camera control in the context of video surveillance" in Image Analysis and Recognition, vol. 5627 LNCS, pp. 657--667, 2009. Google ScholarDigital Library
- P.D.Z. Varcheie and G.A. Bilodeau, "Adaptive fuzzy particle filter tracker for a ptz camera in an ip surveillance system," Instrumentation and Measurement, 60(2):354--371, 2011.Google ScholarCross Ref
- Y. Xie, M. Pei, G. Yu, X. Song, and Y. Jia, "Tracking pedestrians with incremental learned intensity and contour templates for ptz camera visual surveillance," in International Conference on Multimedia and Expo, pp. 1--6, 2011. Google ScholarDigital Library
- F. Chang, G. Zhang, X. Wang, and Z. Chen, "Ptz camera target tracking in large complex scenes," in Congress on Intelligent Control and Automation, pp. 2914--2918, 2010.Google Scholar
- Y. Xie, L. Lin, and Y. Jia, "Tracking objects with adaptive feature patches for ptz camera visual surveillance," in Conference on Pattern Recognition, pp. 1739--1742, 2010. Google ScholarDigital Library
- J. Shi and C. Tomasi, "Good features to track," in Computer Vision and Pattern Recognition, 1994," in Computer Vision and Pattern Recognition, pp. 593--600, 1994.Google Scholar
- D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift," in Computer Vision and Pattern Recognition, pp. 142--149, 2000.Google Scholar
- J. Ning, L. Zhang, D. Zhang, and C. Wu, "Robust meanshift tracking with corrected background-weighted histogram," IET Computer Vision, 6(1):62--69, 2012.Google ScholarCross Ref
- C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey Vision Conference, pp. 147--151, 1988.Google Scholar
- E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," in European Conference on Computer, vol. 3951 LNCS, pp. 430--443, 2006. Google ScholarDigital Library
- E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "Orb: An efficient alternative to sift or surf," in International Conference onComputer Vision, pp. 2564--2571, 2011. Google ScholarDigital Library
- D.G. Lowe, "Distinctive image features from scale-invariant keypoints". International Journal of Computer Vision, 60(2):91--110, 2004. Google ScholarDigital Library
- H. Bay, T. Tuytelaars, L. Van Gool, "Speeded-up robust features (SURF)", Computer vision and image understanding, 110(3):346--359, 2008. Google ScholarDigital Library
- B.D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in DARPA Image Understanding Workshop, pp. 121--130, 1981. Google ScholarDigital Library
- C. Tomasi and T. Kanade, "Detection and tracking of point features", Tech. Rep., Journal of Computer Vision, 1991.Google Scholar
- J. Bouguet, "Pyramidal implementation of the Lucas Kanade feature tracker," Intel Corp., Micro. Research Labs, 2000.Google Scholar
- K.S. Kim, D.S. Jang, and H. Choi, "Real time face tracking with pyramidal Lucas-Kanade feature tracker," in Computational Science and Its Applications, vol. 4705 LNCS, pp. 1074--1082, 2007. Google ScholarDigital Library
- R. Martin and J.M. Martinez, "Correlation study of video object trackers evaluation metrics," IET Electronics Letters, 50(5):361--363, 2014.Google ScholarCross Ref
- R. Kasturi, et al., "Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol," IEEE Trans. on Pattern Analysis and Machine Intelligence, 31(2):319--336, 2009. Google ScholarDigital Library
- R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley Publishing, 2009. Google ScholarDigital Library
- K. Nummiaro, E. Koller-Meier, and L.J. Van Gool, "An adaptive colour-based particle filter," Image and Vision Computing, 21(1):99--110, 2003.Google ScholarCross Ref
- S. Baker and I. Matthews, "Lucas-kanade 20 years on: A unifying framework," International Journal of Computer Vision, 56(3):221--255, 2004. Google ScholarDigital Library
- D. A. Ross, J. Lim, R. S. Lin, and M. H. Yang, "Incremental learning for robust visual tracking," International Journal of Computer Vision, 77(1-3):125--141, 2008. Google ScholarDigital Library
- Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-learningdetection," IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(7):1409--1422, 2011. Google ScholarDigital Library
- J. Ning, L. Zhang, D. Zhang, and C.Wu, "Scale and orientation adaptive mean shift tracking," IET Computer Vision, 6(1):52--61, 2012.Google ScholarCross Ref
Index Terms
- Single Object Long-term Tracker for Smart Control of a PTZ camera
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