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:
Real-Time Semantic Segmentation with Label Propagation Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Fully-Convolutional Siamese Networks for Object Tracking

verfasst von : Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object’s appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
Nächstes Kapitel Speech-Driven Facial Animation Using Manifold Relevance Determination
Literatur
1.
Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. PAMI 36(7), 1442–1468 (2014)CrossRef
2.
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. PAMI 34(7), 1409–1422 (2012)CrossRef
3.
Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV 2011. IEEE (2011)
4.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. PAMI 37(3), 583–596 (2015)CrossRef
5.
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: ICCV (2015)
6.
Danelljan, M., Hager, G., Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: ICCV 2015 Workshop, pp. 58–66 (2015)
7.
Wang, N., Li, S., Gupta, A., Yeung, D.Y.: Transferring rich feature hierarchies for robust visual tracking. arXiv CoRR (2015)
8.
Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: ICCV (2015)
9.
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. arXiv CoRR (2015)
10.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRef
11.
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR 2013 (2013)
12.
Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., Vojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: The visual object tracking VOT2015 challenge results. In: ICCV 2015 Workshop, pp. 1–23 (2015)
13.
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: An astounding baseline for recognition. In: CVPR 2014 Workshop (2014)
14.
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC 2015 (2015)
15.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: FlowNet: Learning optical flow with convolutional networks. In: ICCV 2015 (2015)
16.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS 2012 (2012)
17.
Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: Signature verification using a “Siamese” time delay neural network. Int. J. Pattern Recogn. Artif. Intell. (1993)
18.
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. CVPR 2014, 1701–1708 (2014)
19.
Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: CVPR 2015 (2015)
20.
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR 2015, pp. 815–823 (2015)
21.
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. ICCV 2015, 118–126 (2015)
22.
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML 2015 Deep Learning Workshop (2015)
23.
Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: CVPR 2016, pp. 5695–5703 (2016)
24.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML 2015, pp. 448–456 (2015)
25.
Gan, Q., Guo, Q., Zhang, Z., Cho, K.: First step toward model-free, anonymous object tracking with recurrent neural networks. arXiv CoRR (2015)
26.
Kahou, S.E., Michalski, V., Memisevic, R.: RATM: Recurrent Attentive Tracking Model. arXiv CoRR (2015)
27.
Denil, M., Bazzani, L., Larochelle, H., de Freitas, N.: Learning where to attend with deep architectures for image tracking. Neural Computation (2012)
28.
Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. arXiv CoRR (2016)
29.
Chen, K., Tao, W.: Once for all: a two-flow convolutional neural network for visual tracking. arXiv CoRR (2016)
30.
Tao, R., Gavves, E., Smeulders, A.W.M.: Siamese instance search for tracking. arXiv CoRR (2016)
31.
Vedaldi, A., Lenc, K.: MatConvNet – Convolutional Neural Networks for MATLAB (2015)
32.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV 2015 (2015)
33.
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: Staple: Complementary learners for real-time tracking. In: CVPR 2016 (2016)
34.
Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: CVPR 2015 (2015)
35.
Zhu, G., Wang, J., Wu, Y., Lu, H.: Collaborative correlation tracking. In: BMVC 2015 (2015)
36.
Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: Visual tracking using attention-modulated disintegration and integration. In: CVPR 2016 (2016)
37.
Ning, J., Yang, J., Jiang, S., Zhang, L., Yang, M.H.: Object tracking via dual linear structured SVM and explicit feature map. In: CVPR 2016 (2016)
38.
Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC 2014 (2014)
39.
Huang12, D., Luo, L., Wen12, M., Chen12, Z., Zhang12, C.: Enable scale and aspect ratio adaptability in visual tracking with detection proposals
40.
Liris, F.: The visual object tracking vot2014 challenge results (2014)
41.
Zhu, G., Porikli, F., Li, H.: Tracking randomly moving objects on edge box proposals. arXiv CoRR (2015)
42.
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV 2015, pp. 4310–4318 (2015)
43.
Hua, Y., Alahari, K., Schmid, C.: Online object tracking with proposal selection. ICCV 2015, 3092–3100 (2015)
44.
Wang, N., Yeung, D.Y.: Ensemble-based tracking: aggregating crowdsourced structured time series data. ICML 2014, 1107–1115 (2014)
45.
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV 2014 Workshops (2014)
46.
Li, A., Lin, M., Wu, Y., Yang, M.H., Yan, S.: NUS-PRO: a new visual tracking challenge. PAMI 38(2), 335–349 (2016)CrossRef
47.
Possegger, H., Mauthner, T., Bischof, H.: In defense of color-based model-free tracking. In: CVPR 2015 (2015)
Metadaten
Titel
Fully-Convolutional Siamese Networks for Object Tracking
verfasst von
Luca Bertinetto
Jack Valmadre
João F. Henriques
Andrea Vedaldi
Philip H. S. Torr
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_56