Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Demand-Aware Opportunistic Spectrum Access: A Game-Theoretic Learning Approach Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Adaptive Multi-feature Fusion for Correlation Filter Tracking

verfasst von : Linfeng Liu, Xiaole Yan, Qiu Shen

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Robust visual object tracking is a challenging task in computer vision. Recently correlation filter-based trackers (CFTs) have aroused increasing interests because of the good performance and high efficiency. However, most feature representations for CFTs are not discriminative enough, which makes the trackers unreliable in complicated and changing scenarios. To address the problem, this paper presents an adaptive multi-feature fusion method based on kernelized correlation filter (KCF) framework. First we select HOG, LBP and grayscale feature for fusion to obtain more complementary and powerful feature. Then we propose a novel multi-feature fusion strategy, and adaptively calculate the feature’s fusion weight using probability separability criterion. The experimental results show that our method not only achieves better accuracy compared with existing features for KCF tracker, but also achieves state-of-the-art performance when running at 87 frames per second.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Social Centrality-Aware D2D Multicast Scheme for Content Dissemination
Nächstes Kapitel Research on the Resource Allocation Algorithm in the WFRFT-Based Hybrid-Carrier System
Literatur
1.
Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 983–990 (2009)
2.
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2012, pp. 1830–1837 (2012)
3.
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2010, pp. 2544–2550 (2010)
4.
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
5.
6.
Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, 1–5 September 2014
7.
Danelljan, M., Shahbaz Khan, F., Felsberg, M., Van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097 (2014)
8.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)
9.
Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: 2011 International Conference on Computer Vision, pp. 263–270 (2011)
10.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715 (2012)
11.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 583–596 (2015)
12.
Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2012, pp. 1822–1829 (2012)
13.
Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: bootstrapping binary classifiers by structural constraints. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2010, pp. 49–56 (2010)
14.
Kristan, M., Matas, J., Leonardis, A., et al.: A novel performance evaluation methodology for single-target trackers. IEEE Trans. Pattern Anal. Mach. Intell. (2016)
15.
Kristan, M., Leonardis, A., Matas, J., et al.: The visual object tracking VOT2016 challenge results. In: European Conference on Computer Vision Workshops, pp. 191–217 (2016)
16.
Li, N., Shi, Y.-Z., Zhou, J.-J.: A real-time object tracking algorithm based on self-adaptive feature selection. Opto-Electron. Eng. 7, 002 (2009)
17.
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision, pp. 254–265 (2014)
18.
Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4902–4912 (2015)
19.
Ma, C., Huang, J.-B., Yang, X., Yang, M.-H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)
20.
Ma, C., Yang, X., Zhang, C., Yang, M.-H.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)
21.
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. arXiv preprint arXiv:​1510.​07945 (2015)
22.
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
23.
Stalder, S., Grabner, H., Van Gool, L.: Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1409–1416 (2009)
24.
Sun, Q., Zeng, S., Liu, Y., Heng, P., Xia, D.: A new method of feature fusion and its application in image recognition. Pattern Recognit. 38, 2437–2448 (2005)
25.
Van De Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18, 1512–1523 (2009)
26.
Wang, N., Shi, J., Yeung, D.-Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3101–3109 (2015)
27.
Wu, Y., Lim, J., Yang, M.-H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1834–1848 (2015)
28.
Yuan, G., Xue, M., Han, Y.: Mean shift object tracking based on adaptive multi—features fusion. J. Comput. Res. Develop. 47, 1663–1671 (2010)
29.
Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: European Conference on Computer Vision, pp. 127–141 (2014)
30.
Zhang, K., Zhang, L., Yang, M.-H.: Real-time object tracking via online discriminative feature selection. IEEE Trans. Image Process. 22, 4664–4677 (2013)
31.
Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer vision and Pattern Recognition CVPR 2012, pp. 1838–1845 (2012)
Metadaten
Titel
Adaptive Multi-feature Fusion for Correlation Filter Tracking
verfasst von
Linfeng Liu
Xiaole Yan
Qiu Shen
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_128

Neuer Inhalt

    Bildnachweise