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Parallel implementation of color-based particle filter for object tracking in embedded systems

Authors:

Mai Thanh Truong,

Sanghoon KimAuthors Info & Claims

Human-centric Computing and Information Sciences, Volume 7, Issue 1

Article No.: 82, Pages 1 - 13

https://doi.org/10.1186/s13673-016-0082-1

Published: 01 December 2017 Publication History

Abstract

Recently, embedded systems have become popular because of the rising demand for portable, low-power devices. A common task for these devices is object tracking, which is an essential part of various applications. Until now, object tracking in video sequences remains a challenging problem because of the visual properties of objects and their surrounding environments. Among the common approaches, particle filter has been proven effective in dealing with difficulties in object tracking. In this research, we develop a particle filter based object tracking method using color distributions of video frames as features, and deploy it in an embedded system. Because particle filter is a high-complexity algorithm, we utilize computing power of embedded systems by implementing a parallel version of the algorithm. The experimental results show that parallelization can enhance the performance of particle filter when deployed in embedded systems.

References

[1]

Kenan M, Fei H, Xiangmo Z (2016) Multiple vehicle detection and tracking in highway traffic surveillance video based on sift feature matching. J Inf Process Syst 12:183---195

[2]

Juhyun L, Hanbyul C, Kicheon H (2015) A fainting condition detection system using thermal imaging cameras based object tracking algorithm. J Converg 6:1---15

[3]

Bostanci E, Kanwal N, Clark AF (2015) Augmented reality applications for cultural heritage using kinect. Hum Cent Comput Inf Sci 5:1---18

[4]

Smeulders AWM, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36:1442---1468

Digital Library

[5]

Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38:1---45

Digital Library

[6]

Fiack L, Cuperlier N, Miramond B (2015) Embedded and real-time architecture for bio-inspired vision-based robot navigation. J Real Time Image Process 10:699---722

Digital Library

[7]

Hammoudi K, Benhabiles H, Kasraoui M, Ajam N, Dornaika F, Radhakrishnan K, Bandi K, Cai Q, Liu S (2015) Developing vision-based and cooperative vehicular embedded systems for enhancing road monitoring services. Proced Comp Sci 52:389---395

Digital Library

[8]

Lu F, Lee S, Kumar Satzoda R, Trivedi M (2016) Embedded computing framework for vision-based real-time surround threat analysis and driver assistance. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops, vol 1, pp 83---91

[9]

Varfolomieiev A, Lysenko O (2016) An improved algorithm of median flow for visual object tracking and its implementation on arm platform. J Real Time Image Process 11:527---534

Digital Library

[10]

Isard M, Blake A (1998) Condensation-conditional density propagation for visual tracking. Int J Comput Vis 29:5---28

Digital Library

[11]

Isard M, Blake A (1998) In: Burkhardt H, Neumann B (eds) Icondensation: unifying low-level and high-level tracking in a stochastic framework. Springer, Berlin, pp 893---908

Digital Library

[12]

MacCormick J, Blake A (2000) A probabilistic exclusion principle for tracking multiple objects. Int J Comput Vis 39:57---71

Digital Library

[13]

Wu Y, Huang TS (2004) Robust visual tracking by integrating multiple cues based on co-inference learning. Int J Comput Vis 58:55---71

Digital Library

[14]

Khan Z, Balch T, Dellaert F (2004) A rao-blackwellized particle filter for eigentracking. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, vol 2. CVPR, pp 980---986

Digital Library

[15]

Moulkheir Naoui SM, Belalem G (2016) Feasibility study of a distributed and parallel environment for implementing the standard version of AAM model. J Inf Process Syst 12:149---168

[16]

Stepanov OA, Vasil'ev VA (2016) Cramér-rao lower bound in nonlinear filtering problems under noises and measurement errors dependent on estimated parameters. Autom Remote Control 77:81---105

Digital Library

[17]

Li T, Bolic M, Djuric PM (2015) Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Signal Process Mag 32:70---86

[18]

Barthélemy Q, Larue A, Mars JI (2015) Color sparse representations for image processing: review, models, and prospects. IEEE Trans Image Process 24:3978---3989

Digital Library

[19]

Vojir T, Noskova J, Matas J (2014) Robust scale-adaptive mean-shift for tracking. Pattern Recognit Lett 49:250---258

Digital Library

[20]

Nummiaro K, Koller-Meier E, Van Gool L (2002) In: Van Gool L (ed) Object tracking with an adaptive color-based particle filter. Springer, Berlin, pp 353---360

Digital Library

[21]

Tracking Dataset. http://cmp.felk.cvut.cz/~vojirtom/dataset/

Cited By

Abid NOuni TAmmari AAbid M(2022)Efficient and high-performance pedestrian detection implementation for intelligent vehiclesMultimedia Systems10.1007/s00530-021-00799-128:1(69-84)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00530-021-00799-1
Abid NLoukil KOuni TAyedi WAmmari AAbid M(2020)An improvement of multi-scale covariance descriptor for embedded systemJournal of Real-Time Image Processing10.1007/s11554-018-0759-y17:3(419-435)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11554-018-0759-y
Kwak JPark JSung Y(2019)Affective social big data generation algorithm for autonomous controls by CRNN-based end-to-end controlsMultimedia Tools and Applications10.1007/s11042-019-7703-478:19(27175-27192)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11042-019-7703-4
Show More Cited By

Parallel implementation of color-based particle filter for object tracking in embedded systems
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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cover image Human-centric Computing and Information Sciences

Human-centric Computing and Information Sciences Volume 7, Issue 1

December 2017

729 pages

ISSN:2192-1962

EISSN:2192-1962

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Copyright © Copyright © 2017 The Author(s).

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2017

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Cited By

Abid NOuni TAmmari AAbid M(2022)Efficient and high-performance pedestrian detection implementation for intelligent vehiclesMultimedia Systems10.1007/s00530-021-00799-128:1(69-84)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00530-021-00799-1
Abid NLoukil KOuni TAyedi WAmmari AAbid M(2020)An improvement of multi-scale covariance descriptor for embedded systemJournal of Real-Time Image Processing10.1007/s11554-018-0759-y17:3(419-435)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11554-018-0759-y
Kwak JPark JSung Y(2019)Affective social big data generation algorithm for autonomous controls by CRNN-based end-to-end controlsMultimedia Tools and Applications10.1007/s11042-019-7703-478:19(27175-27192)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11042-019-7703-4
Jeong SLee YLee S(2017)Development of an automatic sorting system for fresh ginsengs by image processing techniquesHuman-centric Computing and Information Sciences10.1186/s13673-017-0122-57:1(1-13)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1186/s13673-017-0122-5

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