LEADER 05189nam 2200673 450 001 9910828345603321 005 20230807212346.0 010 $a1-119-05405-2 010 $a1-119-00486-1 010 $a1-119-05391-9 035 $a(CKB)3710000000331930 035 $a(EBL)1895317 035 $a(SSID)ssj0001441015 035 $a(PQKBManifestationID)11801385 035 $a(PQKBTitleCode)TC0001441015 035 $a(PQKBWorkID)11393949 035 $a(PQKB)11248211 035 $a(MiAaPQ)EBC1895317 035 $a(Au-PeEL)EBL1895317 035 $a(CaPaEBR)ebr11004206 035 $a(CaONFJC)MIL690590 035 $a(OCoLC)899739127 035 $a(EXLCZ)993710000000331930 100 $a20150123h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTracking with particle filter for high-dimensional observation and state spaces /$fSe?verine Dubuisson 210 1$aLondon, England ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (223 p.) 225 1 $aDigital Signal and Image Processing Series 300 $aDescription based upon print version of record. 311 $a1-84821-603-3 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Notations; Introduction; 1: Visual Tracking by Particle Filtering; 1.1. Introduction; 1.2. Theoretical models; 1.2.1. Recursive Bayesian filtering; 1.2.2. Sequential Monte-Carlo methods; 1.2.2.1. Importance sampling; 1.2.2.2. Particle filter; 1.2.3. Application to visual tracking; 1.2.3.1. State model; 1.2.3.2. Observation model; 1.2.3.3. Importance function; 1.2.3.4. Likelihood function; 1.2.3.5. Resampling methods; 1.3. Limits and challenges; 1.4. Scientific position; 1.5. Managing large sizes in particle filtering; 1.6. Conclusion 327 $a2: Data Representation Models2.1. Introduction; 2.2. Computation of the likelihood function; 2.2.1. Exploitation of the spatial redundancy; 2.2.1.1. Optimal order for histogram computation; 2.2.1.2. Optimization of the integral histogram; 2.2.2. Exploitation of the temporal redundancy; 2.2.2.1. Temporal histogram; 2.2.2.2. Incremental distance between histograms; 2.3. Representation of complex information; 2.3.1. Representation of observations for movement detection, appearances and disappearances; 2.3.2. Representation of deformations; 2.3.3. Multifeature representation 327 $a2.3.3.1. Multimodal tracking2.3.3.2. Multifragment tracking; 2.3.3.3. Multiappearance tracking; 2.4. Conclusion; 3: Tracking Models That Focus on the State Space; 3.1. Introduction; 3.2. Data association methods for multi-object tracking; 3.2.1. Particle filter with adaptive classification; 3.2.2. Energetic filter for data association; 3.3. Introducing fuzzy information into the particle filter; 3.3.1. Fuzzy representation; 3.3.2. Fuzzy spatial relations; 3.3.3. Integration of fuzzy spatial relations into the particle filter; 3.3.3.1. Application to tracking an object with erratic movements 327 $a3.3.3.2. Application to multi-object tracking3.3.3.3. Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. Conclusion; 4: Models of Tracking by Decomposition of the State Space; 4.1. Introduction; 4.2. Ranked partitioned sampling; 4.3. Weighted partitioning with permutation of sub-particles; 4.3.1. Permutation of sub-samples; 4.3.2. Decrease the number of resamplings; 4.3.3. General algorithm and results; 4.4. Combinatorial resampling; 4.5. Conclusion; 5: Research Perspectives in Tracking and Managing Large Spaces 327 $a5.1. Tracking for behavioral analysis: toward finer tracking of the "future" and the "now"5.2. Tracking for event detection: toward a top-down model; 5.3. Tracking to measure social interactions; Bibliography; Index 330 $aThis title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions. 410 0$aDigital signal and image processing series. 606 $aComputer vision$xMathematical models 606 $aPattern recognition systems 606 $aParticle methods (Numerical analysis) 615 0$aComputer vision$xMathematical models. 615 0$aPattern recognition systems. 615 0$aParticle methods (Numerical analysis) 676 $a006.37 700 $aDubuisson$b Se?verine$01661729 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910828345603321 996 $aTracking with particle filter for high-dimensional observation and state spaces$94017843 997 $aUNINA