Top

Published in:

2016 | OriginalPaper | Chapter

Recent Developments in Tracking Objects in a Video Sequence

Authors : Michał Staniszewski, Mateusz Kloszczyk, Jakub Segen, Kamil Wereszczyński, Aldona Drabik, Marek Kulbacki

Published in: Intelligent Information and Database Systems

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Methods of tracking of multiple objects or people in video sequences have applications in many fields such as surveillance, art, transport or biology. This, over four decades old area is still very active, with multiple new contributions presented every year. Tracking methods must solve intricate problems, for example occlusion of many objects, crowded scenes, illumination of different places and motion of camera. This paper presents a brief survey of recent developments in video tracking based methods, focused mainly on the last three years. The surveyed methods are divided into two groups: tracking by detection, which includes methods that solve the problem of time-linking objects detected in all video frames, and tracking by correlation, containing methods that follow a selected object using cross correlation. The reviewed methods are collected in a table that lists for each method the benchmark datasets used for its evaluation, implementation environment, and whether it can track single or multiple objects.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now

previous chapter Selected Space-Time Based Methods for Action Recognition

next chapter Recent Developments on 2D Pose Estimation From Monocular Images

Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., van den Hengel, A.: A Survey of Appearance Models in Visual Object Tracking (2013). CoRR abs/1303.4803

Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. Pat. An. Mach. Intel. 36, 1442–1468 (2013)

Wu, Y., Lim, J., Yang, M.-H.: Online Object Tracking: A Benchmark CVpPR 2013, pp. 2411–2418 (2013). http://visual-tracking.net

Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. PAMI 25(5), 564–577 (2003)CrossRef

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)

Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)

Avidan, S.: Support vector tracking. PAMI 26(8), 1064–1072 (2004)CrossRef

Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)

Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. PAMI 25(10), 1296–1311 (2003)CrossRef

10.

Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: PROST: parallel robust online simple tracking. In: CVPR (2010)

11.

Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: ICCV (2009)

12.

Ristani, E., Tomasi, C.: Tracking multiple people online and in real time. In: 12th Asian Conference on Computer Vision, pp. 444–459 (2014). https://www.cs.duke.edu/ristani/bip_tracker.html

13.

Zamir, A.R., Dehghan, A., Shah, M.: GMCP-Tracker: global multi-object tracking using generalized minimum clique graphs. In: Proceedings of the 12th European Conference on Computer Vision, pp. 343–356 (2012). http://crcv.ucf.edu/projects/GMCP-Tracker/

14.

Dicle, C., Camps, O., Sznaier, M.: The Way They Move: Tracking Multiple Targets with Similar Appearance Computer Vision (ICCV) (2013). https://bitbucket.org/cdicle/smot

15.

Rossand, G., Soland, R.: A branch and bound algorithm for the generalized assignment problem. Math. Program. 8(1), 91–103 (1975)MathSciNetCrossRefMATH

16.

Ayazoglu, M., Sznaier, M., Camps, O.: Fast algorithms for structured robust principal component analysis. In: CVPR, pp. 1704–1711 (2012)

17.

Park, H., Zhang, L., Rosen, J.: Low rank approximation of a hankel matrix by structured total least norm. BIT Numer. Math. 39(4), 757–779 (1999)MathSciNetCrossRefMATH

18.

Dehghan, A., Assari, S., Shah, M.: GMMCP tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4091–4099 (2015). http://crcv.ucf.edu/projects/GMMCP-Tracker/

19.

Milan, A., Leal-Taixe, L., Schindler, K., Reid, I.: Joint tracking and segmentation of multiple targets CVPR (2015). https://bitbucket.org/amilan/segtracking

20.

Poiesi, F., Cavallaro, A.: Tracking multiple high-density homogeneous targets. IEEE Trans. Circ. Syst. Video Technol. 25, 623–637 (2015). http://www.eecs.qmul.ac.uk/andrea/thdt.htmlCrossRef

21.

Bae, S.-H., Yoon, K.-J.: Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning CVPR, pp. 1218–1225 (2014). https://cvl.gist.ac.kr/project/cmot.html

22.

Kim, T.-K., Stenger, B., Kittler, J., Cipolla, R.: Incremental linear discriminant analysis using sufficient spanning sets and its applications. IJCV 91(2), 216–232 (2011)MathSciNetCrossRefMATH

23.

Danelljan, M., Hager, G., Shahbaz, K., F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference (2014). http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/index.html

24.

Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Computer Vision and Pattern Recognition (2010)

25.

Hare, S., Saffari, A., Torr, P.: Struck: structured output tracking with kernels. In: Computer Vision and Pattern Recognition (2011)

26.

Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: Computer Vision and Pattern Recognition (2012)

27.

Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity based collaborative model. In: Computer Vision and Pattern Recognition (2012)

28.

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 886–893 (2005)

29.

Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.H.: Fast visual tracking via dense spatio-temporal context learning. In: 13th European Conference, Zurich, pp. 127–141 (2014). http://www4.comp.polyu.edu.hk/cslzhang/STC/STC.htm

30.

Henriques, J.F., Caseiro, R., Martins, P., Batista J.: High-Speed Tracking with Kernelized Correlation Filters, CoRR (2014). abs/1404.7584http://home.isr.uc.pt/henriques/circulant/

31.

Felzenszwalb, P., Girshick, R., McAllester, B., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRef

32.

Ferryman, J.: Proceedings (pets 2009). Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2009)

33.

Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: Computer Vision and Pattern Recognition (2011)

34.

Andriluka, M., Roth, S., Schiele, B.: People-tracking-bydetectionandpeople-detection-by-tracking. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 1–8 (2008)

35.

Ess, A., Leibe, B., Schindler, K., Van Gool, L.: A mobile vision system for robust multi-person tracking. In: Computer Vision and Pattern Recognition (2008)

Title: Recent Developments in Tracking Objects in a Video Sequence
Authors: Michał Staniszewski
Mateusz Kloszczyk
Jakub Segen
Kamil Wereszczyński
Aldona Drabik
Marek Kulbacki
Publisher: Springer Berlin Heidelberg
Book: Intelligent Information and Database Systems
Print ISBN: 978-3-662-49389-2

Electronic ISBN: 978-3-662-49390-8

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-662-49390-8_42

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner