Sequential Monte Carlo tracking by fusing multiple cues in video sequences

被引：113
|
作者：
Brasnett, Paul [1 ]
Mihaylova, Lyudmila
Bull, David
Canagarajah, Nishan
机构：
[1] Univ Lancaster, Dept Commun Syst, Lancaster LA1 4WA, England
[2] Univ Bristol, Dept Elect & Elect Engn, Bristol BS8 1UB, Avon, England
来源：
IMAGE AND VISION COMPUTING | 2007年 / 25卷 / 08期
关键词：
particle filtering; tracking in video sequences; colour; texture; edges; multiple cues; Bhattacharyya distance;
D O I：
10.1016/j.imavis.2006.07.017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：1217 / 1227
页数：11
相关论文
共 50 条
  • [31] Distributed tracking with sequential Monte Carlo methods for manoeuvrable sensors
    Jaward, M. H.
    Bull, D.
    Canagarajah, N.
    NSSPW: NONLINEAR STATISTICAL SIGNAL PROCESSING WORKSHOP: CLASSICAL, UNSCENTED AND PARTICLE FILTERING METHODS, 2006, : 113 - 116
  • [32] SEQUENTIAL MONTE CARLO RADIO-FREQUENCY TOMOGRAPHIC TRACKING
    Li, Yunpeng
    Chen, Xi
    Coates, Mark
    Yang, Bo
    2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, : 3976 - 3979
  • [33] Eye tracking with statistical learning and sequential Monte Carlo sampling
    Huang, W
    Kwan, CW
    De Silva, LC
    ICICS-PCM 2003, VOLS 1-3, PROCEEDINGS, 2003, : 1873 - 1878
  • [34] Tracking interacting subcellular structures by sequential Monte Carlo method
    Wen, Quan
    Gao, Jean
    2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, 2007, : 4185 - 4188
  • [35] Novel sequential monte carlo method to bearing only tracking
    Qu, Hongquan
    Li, Shaohong
    PIERS 2007 BEIJING: PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM, PTS I AND II, PROCEEDINGS, 2007, : 701 - +
  • [36] Object tracking based on snake and sequential Monte Carlo method
    Tan, Hui
    Chen, Xinmeng
    Jiang, Min
    ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 2, 2006, : 364 - +
  • [37] SEQUENTIAL MONTE CARLO TECHNIQUES FOR EEG DIPOLE PLACING AND TRACKING
    Mohseni, Hamid R.
    Wilding, Edward L.
    Sanei, Saeid
    2008 IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP, 2008, : 95 - +
  • [38] Sequential Quantum Monte-Carlo for Tracking of Indistinguishable Targets
    Ulmke, Martin
    2018 SYMPOSIUM ON SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF), 2018,
  • [39] APPLICATION OF SEQUENTIAL MONTE CARLO METHODS FOR SPACE OBJECT TRACKING
    Hussein, Islam I.
    Zaidi, Waqar
    Faber, Weston
    Roscoe, Christopher W. T.
    Wilkins, Matthew P.
    Schumacher, Paul W., Jr.
    Bolden, Mark
    SPACEFLIGHT MECHANICS 2017, PTS I - IV, 2017, 160 : 1313 - 1328
  • [40] Mobility tracking in cellular networks with sequential Monte Carlo filters
    Mihaylova, L
    Bull, D
    Angelova, D
    Canagarajah, N
    2005 7TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), VOLS 1 AND 2, 2005, : 107 - 114
12345