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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Programming and Computer Software
Erschienen in: Programming and Computer Software 4/2019

01.07.2019

A Distributed Tracking Algorithm for Counting People in Video

verfasst von: D. A. Kuplyakov, E. V. Shalnov, V. S. Konushin, A. S. Konushin

Erschienen in: Programming and Computer Software | Ausgabe 4/2019

Einloggen

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

search-config
loading …

Abstract

The problem of tracking people in a video stream with the aim of counting them is studied. Modern video surveillance systems, such as the Moscow video surveillance system, use hundreds of thousands of cameras. The use of modern methods developed for working on a single computer with an expensive graphical processor is economically inefficient for such large-scale systems. In this paper, a distributed tracking algorithm is proposed. It makes it possible to reduce the amount of computational resources due to detecting people in a sparse set of frames. The detection is performed on servers installed in a data center, while the video stream is processed by local camera computation nodes. The experimental evaluation showed that the proposed algorithm provides acceptable quality at the detection rate of 4/3 Hz.
Vorheriger Artikel Visualization Methods and Algorithms for Graph Representation of Functional Programs
Nächster Artikel Detection of Brain Cells in Optical Microscopy Based on Textural Features with Machine Learning Methods

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

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+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 "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
Literatur
1.
Fillipov, I.V. et al., Counting people in a video sequence based on detecting the head of each person, Program. Producty Sist., 2015, no. 1, pp. 121–126.
2.
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B., Simple online and realtime tracking, Comput. Vision Pattern Recognit., 2016. arXiv:1602.00763 [cs.CV]
3.
Fengwei, Y., Wenbo, L., Quanquan, L., Yu, L., Xiaohua, S., and Junjie, Y., Poi: Multiple object tracking with high performance detection and appearance feature, European Conference on Computer Vision, Springer, 2016, pp. 36–42.
4.
Wojke, N., Bewley, A., and Paulus, D., Simple Online and realtime tracking with a deep association metric, Comput. Vision Pattern Recognit., 2017. arXiv:1703. 07402 [cs.CV]
5.
Shu, G. et al. Part-based multiple-person tracking with partial occlusion handling, IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1815–1821.
6.
Benfold, B. and Reid, I., Stable multi-target tracking in real-time surveillance video, IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 3457–3464.
7.
Shalnov, E., Konushin, V., and Konushin, A., An improvement on an MCMC-based video tracking algorithm, Pattern Recognit. Image Anal., 2015, vol. 25, pp. 532–540.CrossRef
8.
Kuplyakov, D., Shalnov, E., and Konushin, A., Markov chain Monte Carlo based video tracking algorithm, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 224–229.MathSciNetCrossRef
9.
Choi, W., Near-online multi-target tracking with aggregated local flow descriptor, Comput. Vision Pattern Recogn., 2015. arXiv:1504.02340 [cs.CV]
10.
11.
Lenz P., Geiger A., and Urtasun, R., FollowMe: Efficient online min-cost flow tracking with bounded memory and computation, IEEE International Conference on Computer Vision (ICCV), 2015.
12.
Shalnov, E.V., Konushin, A.S., and Konushin, V.S., Convolutional neural network for camera pose estimation from object detections, ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, vol. 42, pp. 1–6.
13.
Shalnov, E.V. and Konushin, A.S., Using scene geometry to improve detector accuracy, Program. Producty Sist., 2017, vol. 30, no. 1, pp. 106–111.
14.
Ren, S. et al., Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 2015, pp. 91–99.
15.
Kristan, M. et al., The Visual Object Tracking VOT2016 challenge results, 2016. http://​www.​springer.​ com/gp/book/9783319488806
16.
Vojir,T., Noskova, J., and Matas, J., Robust scale-adaptive mean-shift for tracking, Scandinavian Conference on Image Analysis, Springer, 2013, pp. 652–663.
17.
Milan, A. et al., MOT16: A benchmark for multi- object tracking. http://​arxiv.​org/​abs/​1603.​00831
Metadaten
Titel
A Distributed Tracking Algorithm for Counting People in Video
verfasst von
D. A. Kuplyakov
E. V. Shalnov
V. S. Konushin
A. S. Konushin
Publikationsdatum
01.07.2019
Verlag
Pleiades Publishing
Erschienen in
Programming and Computer Software / Ausgabe 4/2019
Print ISSN: 0361-7688
Elektronische ISSN: 1608-3261
DOI
https://doi.org/10.1134/S0361768819040042

Weitere Artikel der Ausgabe 4/2019

Programming and Computer Software 4/2019 Zur Ausgabe

OriginalPaper

Level of Detail for Precomputed Procedural Textures

OriginalPaper

Detection of Brain Cells in Optical Microscopy Based on Textural Features with Machine Learning Methods

OriginalPaper

Visualization Methods and Algorithms for Graph Representation of Functional Programs

OriginalPaper

Algorithms and Software Increasing the Efficiency of Processing Multidimensional Heterogeneous Data

OriginalPaper

Methods of Gait Recognition in Video

OriginalPaper

A Nonlocal Image Denoising Algorithm Using the Structural Similarity Metric

Premium Partner

    Bildnachweise