Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global...
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Published in | IEEE transactions on image processing Vol. 16; no. 5; pp. 1457 - 1469 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York, NY
IEEE
01.05.2007
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 1057-7149 1941-0042 |
DOI | 10.1109/TIP.2007.894233 |
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Abstract | Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point |
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AbstractList | Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point.Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point. |
Author | Chunhua Shen van den Hengel, A. Brooks, M.J. |
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Cites_doi | 10.1109/TCOM.1967.1089532 10.1109/TPAMI.2003.1195991 10.1109/34.400568 10.1109/CVPR.2004.1315113 10.1109/CVPR.2003.1211432 10.1162/neco.1996.8.1.129 10.1109/CVPR.2003.1211475 10.7551/mitpress/7132.001.0001 10.1109/CVPR.2005.73 10.1214/aos/1016218224 10.1145/361002.361007 10.1214/009053604000000715 10.1109/CVPR.2003.1211338 10.1287/moor.1060.0194 10.1023/B:VISI.0000022287.61260.b0 10.1007/978-94-015-7744-1 10.1109/TPAMI.2005.59 10.1007/978-3-642-17146-8 10.1109/TIP.2004.836152 10.1109/ICCV.2005.94 10.1007/978-1-4757-6465-9 10.1109/ICCV.2003.1238383 10.1109/ICCV.2003.1238382 10.1109/TPAMI.2004.1262335 10.1109/TIT.1975.1055330 10.1080/01621459.1996.10476701 10.1109/TPAMI.2003.1177159 10.1007/BF01589116 10.1109/34.1000236 10.1023/A:1008923215028 10.1109/CVPR.2004.1315112 10.1214/aoms/1177729694 10.1023/B:VISI.0000043757.18370.9c 10.1109/TIP.2004.838707 10.1109/CVPR.2005.242 |
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References | ref35 ref13 ref12 carreira-perpin (ref36) 2003 ref15 ref14 ref31 ref30 ref33 ref11 ref32 ref10 wang (ref44) 2003 ref2 ref1 ref39 ref17 hrdle (ref20) 2004 ref19 ref18 hettich (ref37) 1999 okuma (ref43) 2004; 1 ortiz (ref25) 1999 yang (ref7) 2005; 1 ref24 ref45 ref42 prez (ref16) 2002; 2350 ref41 ref22 ref21 salakhutdinov (ref26) 2003 ref28 ref27 ref29 ref8 yang (ref23) 2003; 3 ref9 ref4 ref3 ref6 ref5 ref40 shen (ref38) 2005 blake (ref34) 1987 |
References_xml | – ident: ref41 doi: 10.1109/TCOM.1967.1089532 – ident: ref2 doi: 10.1109/TPAMI.2003.1195991 – ident: ref9 doi: 10.1109/34.400568 – year: 2003 ident: ref36 publication-title: On the number of modes of a Gaussian mixture Tech Rep EDI-INF-RR-0159 – ident: ref14 doi: 10.1109/CVPR.2004.1315113 – ident: ref4 doi: 10.1109/CVPR.2003.1211432 – volume: 3 start-page: 447 year: 2003 ident: ref23 article-title: mean-shift analysis using quasi-newton methods publication-title: Proc IEEE Int Conf Image Processing – ident: ref24 doi: 10.1162/neco.1996.8.1.129 – ident: ref3 doi: 10.1109/CVPR.2003.1211475 – year: 1987 ident: ref34 publication-title: Visual Reconstruction doi: 10.7551/mitpress/7132.001.0001 – year: 1999 ident: ref37 publication-title: The UCI KDD archive Tech Rep – ident: ref22 doi: 10.1109/CVPR.2005.73 – ident: ref32 doi: 10.1214/aos/1016218224 – volume: 1 start-page: 176 year: 2005 ident: ref7 article-title: efficient spatial-feature tracking via the mean-shift and a new similarity measure publication-title: Proc IEEE Conf Computer Vision Pattern Recognition – ident: ref21 doi: 10.1145/361002.361007 – ident: ref30 doi: 10.1214/009053604000000715 – ident: ref35 doi: 10.1109/CVPR.2003.1211338 – ident: ref45 doi: 10.1287/moor.1060.0194 – ident: ref6 doi: 10.1023/B:VISI.0000022287.61260.b0 – year: 2005 ident: ref38 article-title: adaptive over-relaxed mean shift publication-title: 8th Int Symp Signal Process Applications – ident: ref17 doi: 10.1007/978-94-015-7744-1 – ident: ref11 doi: 10.1109/TPAMI.2005.59 – year: 2004 ident: ref20 publication-title: Nonparametric and Semiparametric Models doi: 10.1007/978-3-642-17146-8 – ident: ref12 doi: 10.1109/TIP.2004.836152 – ident: ref27 doi: 10.1109/ICCV.2005.94 – ident: ref33 doi: 10.1007/978-1-4757-6465-9 – ident: ref31 doi: 10.1109/ICCV.2003.1238383 – ident: ref1 doi: 10.1109/ICCV.2003.1238382 – ident: ref39 doi: 10.1109/TPAMI.2004.1262335 – volume: 2350 start-page: 661 year: 2002 ident: ref16 article-title: color-based probabilistic tracking publication-title: Proc Eur Conf Computer Vision – ident: ref8 doi: 10.1109/TIT.1975.1055330 – ident: ref29 doi: 10.1080/01621459.1996.10476701 – ident: ref28 doi: 10.1109/TPAMI.2003.1177159 – ident: ref40 doi: 10.1007/BF01589116 – ident: ref10 doi: 10.1109/34.1000236 – start-page: 512 year: 1999 ident: ref25 article-title: accelerating em: an empirical study publication-title: Proc Uncertainty in Artificial Intell – ident: ref18 doi: 10.1023/A:1008923215028 – start-page: 581 year: 2003 ident: ref44 article-title: false-peaks-avoiding mean shift method for unsupervised peak-valley sliding image segmentation publication-title: Proc 8th Digital Image Computing Techniques and Applications – volume: 1 start-page: 28 year: 2004 ident: ref43 article-title: a boosted particle filter: multitarget detection and tracking publication-title: Proc Eur Conf Computer Vision – ident: ref5 doi: 10.1109/CVPR.2004.1315112 – ident: ref42 doi: 10.1214/aoms/1177729694 – start-page: 664 year: 2003 ident: ref26 article-title: adaptive overrelaxed bound optimization methods publication-title: Proc Int Conf Machine Learning – ident: ref19 doi: 10.1023/B:VISI.0000043757.18370.9c – ident: ref13 doi: 10.1109/TIP.2004.838707 – ident: ref15 doi: 10.1109/CVPR.2005.242 |
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Snippet | Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its... In this way, the global maximum is more reliably located. Since it is imperative that the computational complexity is minimal for real-time applications, such... |
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SubjectTerms | Algorithms Annealing Applied sciences Artificial Intelligence Bandwidth Computational complexity Density Density functional theory Design methodology Exact sciences and technology fast mean shift (MS) global density mode Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Information, signal and communications theory Kernel Mathematical analysis Mathematical models Monte Carlo methods Motion Optimization methods Pattern Recognition, Automated - methods Position (location) Reproducibility of Results Sensitivity and Specificity Signal processing Studies Target tracking Telecommunications and information theory Temperature Tracking Video Recording - methods visual localization visual tracking |
Title | Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking |
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