tracking with particle filter for high dimensional observation and state spaces

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Tracking With Particle Filter For High Dimensional Observation And State Spaces

Author : Séverine Dubuisson
ISBN : 9781119053910
Genre : Technology & Engineering
File Size : 39. 85 MB
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This 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.

Computer Analysis Of Images And Patterns

Author : Richard Wilson
ISBN : 9783642402616
Genre : Computers
File Size : 30. 89 MB
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The two volume set LNCS 8047 and 8048 constitutes the refereed proceedings of the 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013, held in York, UK, in August 2013. The 142 papers presented were carefully reviewed and selected from 243 submissions. The scope of the conference spans the following areas: 3D TV, biometrics, color and texture, document analysis, graph-based methods, image and video indexing and database retrieval, image and video processing, image-based modeling, kernel methods, medical imaging, mobile multimedia, model-based vision approaches, motion analysis, natural computation for digital imagery, segmentation and grouping, and shape representation and analysis.

Proceedings

Author : Institute of Electrical and Electronics Engineers
ISBN : 0769511430
Genre : Computer vision
File Size : 78. 5 MB
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This two-volume set contains the proceedings of the July 2001 conference on computer vision. The 205 papers discuss sensors and early vision, stereo and multiple views, segmentation and matching, learning in vision, shape representation and recovery, stereo and multiple views, segmentation and matching, object recognition, tracking, video analysis, reflectance, image databases, vision systems and texture, and demo overviews. There is no subject index. The included CD-ROM contains a full version of the proceedings. c. Book News Inc.

Eighth Ieee International Conference On Computer Vision

Author :
ISBN : 0769511430
Genre : Computers
File Size : 82. 30 MB
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Proceedings

Author : IEEE Computer Society
ISBN : 0769511430
Genre : Computer vision
File Size : 90. 36 MB
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This two-volume set contains the proceedings of the July 2001 conference on computer vision. The 205 papers discuss sensors and early vision, stereo and multiple views, segmentation and matching, learning in vision, shape representation and recovery, stereo and multiple views, segmentation and matching, object recognition, tracking, video analysis, reflectance, image databases, vision systems and texture, and demo overviews. There is no subject index. The included CD-ROM contains a full version of the proceedings. c. Book News Inc.

Adaptive High Resolution Sensor Waveform Design For Tracking

Author : Ioannis Kyriakides
ISBN : 9781608455768
Genre : Mathematics
File Size : 32. 32 MB
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Recent innovations in modern radar for designing transmitted waveforms, coupled with new algorithms for adaptively selecting the waveform parameters at each time step, have resulted in improvements in tracking performance. Of particular interest are waveforms that can be mathematically designed to have reduced ambiguity function sidelobes, as their use can lead to an increase in the target state estimation accuracy. Moreover, adaptively positioning the sidelobes can reveal weak target returns by reducing interference from stronger targets. The manuscript provides an overview of recent advances in the design of multicarrier phase-coded waveforms based on Bjorck constant-amplitude zero-autocorrelation (CAZAC) sequences for use in an adaptive waveform selection scheme for mutliple target tracking. The adaptive waveform design is formulated using sequential Monte Carlo techniques that need to be matched to the high resolution measurements. The work will be of interest to both practitioners and researchers in radar as well as to researchers in other applications where high resolution measurements can have significant benefits. Table of Contents: Introduction / Radar Waveform Design / Target Tracking with a Particle Filter / Single Target tracking with LFM and CAZAC Sequences / Multiple Target Tracking / Conclusions

Aaai 08 Iaai 08 Proceedings

Author :
ISBN : 1577353684
Genre : Artificial intelligence
File Size : 57. 89 MB
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Augmented Vision Perception In Infrared

Author : Riad I. Hammoud
ISBN : 9781848002777
Genre : Technology & Engineering
File Size : 39. 31 MB
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Throughout much of machine vision’s early years the infrared imagery has suffered from return on investment despite its advantages over visual counterparts. Recently, the ?scal momentum has switched in favor of both manufacturers and practitioners of infrared technology as a result of today’s rising security and safety challenges and advances in thermographic sensors and their continuous drop in costs. This yielded a great impetus in achieving ever better performance in remote surveillance, object recognition, guidance, noncontact medical measurements, and more. The purpose of this book is to draw attention to recent successful efforts made on merging computer vision applications (nonmilitary only) and nonvisual imagery, as well as to ?ll in the need in the literature for an up-to-date convenient reference on machine vision and infrared technologies. Augmented Perception in Infrared provides a comprehensive review of recent deployment of infrared sensors in modern applications of computer vision, along with in-depth description of the world’s best machine vision algorithms and intel- gent analytics. Its topics encompass many disciplines of machine vision, including remote sensing, automatic target detection and recognition, background modeling and image segmentation, object tracking, face and facial expression recognition, - variant shape characterization, disparate sensors fusion, noncontact physiological measurements, night vision, and target classi?cation. Its application scope includes homeland security, public transportation, surveillance, medical, and military. Mo- over, this book emphasizes the merging of the aforementioned machine perception applications and nonvisual imaging in intensi?ed, near infrared, thermal infrared, laser, polarimetric, and hyperspectral bands.

Exploitation De R Seaux Bay Siens Dynamiques Et Du Filtre Particulaire Pour Le Suivi D Objets Articul S

Author : Xuan Son Nguyen
ISBN : OCLC:864566189
Genre :
File Size : 57. 1 MB
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Articulated object tracking has now become a very active research area inthe field of computer vision. One of its applications, i.e. human track-ing, is used in a variety of domains, such as security surveillance, humancomputer interface, gait analysis,...The problem is also of interest from thetheoretical point of view. Some of its challenges include, for example, thehigh dimensionality of state spaces, self-occlusions, kinematic ambiguities orsingularities, making it hard to solve and hence, attractive for the trackingcommunity. Particle Filter (PF) has been shown to be an effective method for solving visual tracking problems. This is due to its ability to deal with non-linear, non-Gaussian and multimodal distributions encountered in such problems. The key idea of particle filter is to approximate the posterior distribution of the target object state by a set of weighted samples. These samples evolve using a proposal distribution and their weights are updated by involving new observations. Under some assumptions, it can be shown that the distribution estimated by particle filter converges in a statistical sense to thetarget distribution. Unfortunately, in high dimensional problems, such asarticulated object tracking problems, the number of samples required for ap-proximating the target distribution can be prohibitively large since it growsexponentially with the number of dimensions (e.g., the number of partsof the object), making the particle filter impractical. To reduce the com-plexity of tracking algorithms in such problems, various methods have beenproposed. One family of approaches that has attracted many researchers isbased on the decomposition of the state space into smaller dimensional sub-spaces where tracking can be achieved using classical methods. This resultsin tracking algorithms that are linear instead of exponential in the numberof parts of the object. Bayesian Networks (BN) offer a very effective way to represent articulated objects and to express the relationship between their different partssince the object can be naturally modeled by graphs where each part of theobject is represented by a node and the physical link between two neighborparts is represented by an edge. Most of conditional independence relation-ships induced by the structural constraints of the articulated objects can beeasily encoded in BN. This kind of graphical model has been exploited forarticulated object tracking in many works and has been shown to be a pow-erful tool for modeling the tracking problem in decomposition approaches.In this thesis, we focus on articulated object tracking and decompositiontechniques to deal with the high dimensionality of the state space describingthe problem. Our first approach is based on the state-of-the-art algorithmfor articulated object tracking: Partition Sampling (PS). First, we developan algorithm called Swapping-Based Partitioned Sampling (SBPS). In thisalgorithm, the prediction/correction step of PF is performed for a groupof parts in parallel instead of part after part as in PS. We also introducean operation called swapping which produces better particles, i.e., particlesthat are nearer to the modes of the target distribution, after the correctionstep of the algorithm. We provide a principled way to select the set of partsprocessed in parallel and to perform the swapping operation so that the pos-terior distribution is correctly estimated. This approach enables to reducethe number of resampling steps of PS and to increase the tracking accu-racy due to the higher number of particles near the modes of the posteriordistribution obtained by the swapping operation.Because the swapping operation generates more particles near the modesof the posterior distribution, this might lead to a situation where the poste-rior distribution is represented by only a few distinct particles, that inducesa loss of particle diversity, resulting in tracking failure in some cases, e.g.when there is a sudden change in movements of the object. To address thisproblem, we introduce an algorithm called DBN-Based Combinatorial Re-sampling for articulated object tracking. Adding this resampling scheme intoa particle filter produces a new algorithm called Particle Filter with Combi-natorial Resampling (PFCR). Instead of aiming to find the best swapping,we create a particle set which contains particles generated from all possiblepermutations, implicitly constructed to avoid resampling over a particle setof exponential size. This approach allows increasing the number of parti-cles near the modes of the posterior distribution but also the diversity ofparticles as compared to SBPS, thus improving the tracking accuracy andreducing tracking failure.Our third approach for articulated object tracking, introduced in thisthesis, is based on a hierarchical search and Particle Swarm Optimization(PSO). This approach called Hierarchical Annealed Particle Swarm Opti-mization Particle Filter (HAPSOPF) aims to increase the tracking accuracyand reduce the computational cost of the tracking algorithm by integratingthe benefits of these two methods. First, the searching efficiency is improvedby performing PSO in subspaces whose dimension is much lower than thatof the original space and therefore the tracking accuracy is increased. Second, the search is performed in the same manner as PS, leading to a reducednumber of particles required for tracking. As a result, the computationalcost of the tracking algorithm is reduced. Moreover, some important factorsare introduced into the update equations of PSO to deal with the problemof noisy observation in articulated object tracking.

Proceedings Of The Ieee Instrumentation And Measurement Technology Conference

Author :
ISBN : UIUC:30112043460788
Genre : Electronic instruments
File Size : 54. 53 MB
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