메뉴 건너뛰기




Volumn 37, Issue 1, 2013, Pages 47-67

A competitive neural network for multiple object tracking in video sequence analysis

Author keywords

Feature selection; Feature weigthing; Genetic Algorithms; Growing competitive neural networks; Multiple object tracking

Indexed keywords

ADAPTIVE FEATURES; COMPETITIVE NEURAL NETWORK; DYNAMIC CHANGES; FEATURE WEIGTHING; MOVING OBJECTS; MOVING VEHICLES; MULTIPLE OBJECT TRACKING; MULTIPLE OBJECTS; OBJECT TRACKING; REAL SITUATION; RESEARCH ISSUES; SELECTION MECHANISM; STANDARD KALMAN FILTERS; TRACKING ALGORITHM;

EID: 84873408263     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-012-9268-3     Document Type: Conference Paper
Times cited : (14)

References (28)
  • 6
    • 36348980559 scopus 로고    scopus 로고
    • Neural network approach to background modeling for video object segmentation
    • 10.1109/TNN.2007.896861
    • Culibrk D, Marques OF, Socek D, Kalva H, Furht B (2007) Neural network approach to background modeling for video object segmentation. IEEE Trans Neural Netw 18(6):1614-1627
    • (2007) IEEE Trans Neural Netw , vol.18 , Issue.6 , pp. 1614-1627
    • Culibrk, D.1    Marques, O.F.2    Socek, D.3    Kalva, H.4    Furht, B.5
  • 7
    • 0001334115 scopus 로고
    • The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination
    • Morgan Kaufmann, San Francisco, CA
    • Eshelman LJ (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Foundations of genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 265-283
    • (1991) Foundations of Genetic Algorithms , pp. 265-283
    • Eshelman, L.J.1
  • 12
    • 0037276988 scopus 로고    scopus 로고
    • Tuning of the structure and parameters of a neural network using an improved genetic algorithm
    • 10.1109/TNN.2002.804317
    • Leung F, Lam H, Ling S, Tam P (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79-88
    • (2003) IEEE Trans Neural Netw , vol.14 , Issue.1 , pp. 79-88
    • Leung, F.1    Lam, H.2    Ling, S.3    Tam, P.4
  • 13
    • 7444243389 scopus 로고    scopus 로고
    • Statistical modeling of complex backgrounds for foreground object detection
    • 10.1109/TIP.2004.836169
    • Liyuan L, Huang W, Gu I, Tian Q (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13(11):1459-1472
    • (2004) IEEE Trans Image Process , vol.13 , Issue.11 , pp. 1459-1472
    • Liyuan, L.1    Huang, W.2    Gu, I.3    Tian, Q.4
  • 14
    • 79952329022 scopus 로고    scopus 로고
    • Stochastic approximation for background modelling
    • 10.1016/j.cviu.2011.01.007
    • López-Rubio E, Luque-Baena RM (2011) Stochastic approximation for background modelling. Comput Vision Image Understand 115(6):735-749
    • (2011) Comput Vision Image Understand , vol.115 , Issue.6 , pp. 735-749
    • López-Rubio, E.1    Luque-Baena, R.M.2
  • 15
    • 79960062885 scopus 로고    scopus 로고
    • Foreground detection in video sequences with probabilistic self-organizing maps
    • 10.1142/S012906571100281X
    • Lopez-Rubio E, Luque-Baena RM, Dominguez E (2011) Foreground detection in video sequences with probabilistic self-organizing maps. Int J Neural Syst 21(3):225-246
    • (2011) Int J Neural Syst , vol.21 , Issue.3 , pp. 225-246
    • Lopez-Rubio, E.1    Luque-Baena, R.M.2    Dominguez, E.3
  • 16
    • 47749109920 scopus 로고    scopus 로고
    • A neural network approach for video object segmentation in traffic surveillance
    • Springer (ed) Springer, New York
    • Luque RM, Domínguez EP, Muñoz J (2008) A neural network approach for video object segmentation in traffic surveillance. In: Springer (ed) Lecture notes in computer science, vol 5112. Springer, New York, pp 151-158
    • (2008) Lecture Notes in Computer Science , vol.5112 , pp. 151-158
    • Luque Rm, D.1
  • 17
    • 79957944156 scopus 로고    scopus 로고
    • An art-type network approach for video object detection
    • Luque R, Dominguez E, Palomo E, Muñoz J (2010) An art-type network approach for video object detection. In: European symposium on artificial neural networks, pp 423-428
    • (2010) European Symposium on Artificial Neural Networks , pp. 423-428
    • Luque R, D.1
  • 20
    • 0035671143 scopus 로고    scopus 로고
    • Detecting perceptual color changes from sequential images for scene surveillance
    • Rautiainen M, Ojala T, Kauniskangas H (2001) Detecting perceptual color changes from sequential images for scene surveillance. IEICE Trans Inf Syst E84D(12):1676-1683
    • (2001) IEICE Trans Inf Syst , vol.84 , Issue.12 , pp. 1676-1683
    • Rautiainen, M.1    Ojala, T.2    Kauniskangas, H.3
  • 22
    • 0024895461 scopus 로고
    • A note on genetic algorithms for large-scale feature selection
    • 0942.68690 10.1016/0167-8655(89)90037-8
    • Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recog Lett 10(5):335-347
    • (1989) Pattern Recog Lett , vol.10 , Issue.5 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2
  • 23
    • 0028409149 scopus 로고
    • Adaptive probabilities of crossover and mutation in genetic algorithms
    • 10.1109/21.286385
    • Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybernet 24(4):656-667
    • (1994) IEEE Trans Syst Man Cybernet , vol.24 , Issue.4 , pp. 656-667
    • Srinivas, M.1    Patnaik, L.2
  • 24
    • 0028446271 scopus 로고
    • Genetic algorithms: A survey
    • 10.1109/2.294849 10.1109/2.294849
    • Srinivas M, Patnaik L (1994) Genetic algorithms: a survey. Computer 27(6):17-26. doi: 10.1109/2.294849
    • (1994) Computer , vol.27 , Issue.6 , pp. 17-26
    • Srinivas, M.1    Patnaik, L.2
  • 25
    • 0034244889 scopus 로고    scopus 로고
    • Learning patterns of activity using real time tracking
    • 10.1109/34.868677
    • Stauffer C, Grimson W (2000) Learning patterns of activity using real time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747-767
    • (2000) IEEE Trans Pattern Anal Mach Intell , vol.22 , Issue.8 , pp. 747-767
    • Stauffer, C.1    Grimson, W.2
  • 27
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • 10.1109/5254.671091
    • Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. Intell Syst Appli IEEE 13(2):44-49
    • (1998) Intell Syst Appli IEEE , vol.13 , Issue.2 , pp. 44-49
    • Yang, J.1    Honavar, V.2
  • 28
    • 4344580597 scopus 로고    scopus 로고
    • Tracking multiple humans in complex situations
    • 10.1109/TPAMI.2004.73
    • Zhao T, Nevatia R (2004) Tracking multiple humans in complex situations. IEEE Trans Pattern Anal Mach Intell 26(9):1208-1221
    • (2004) IEEE Trans Pattern Anal Mach Intell , vol.26 , Issue.9 , pp. 1208-1221
    • Zhao, T.1    Nevatia, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.