Mixed-mode Implementation of Particle Filters

Velmurugan, Rajbabu; Subramanian, S.; Cevher, Volkan; McClellan, J.H.; Anderson, David V.

doi:10.1109/pacrim.2007.4313312

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…, (6) where CPF refers to CONDENSATION and PSA refers to the proposed sampling algorithm. The saved amount of time ranges between 36% and 67% which indicates a considerable reduction in computation achieved by the proposed algorithm.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…The column that is labeled "nb frames" represents the total number of frames in the sequence, and the column labeled "saved comp" represents the approximate percentage of particles not computed for a given sequence when comparing the proposed algorithm to CONDENSATION. This percentage is calculated as shown in (6).…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…Consequently, several papers have investigated ways to achieve low computation tracking with particle filters. These include the works presented by Velmurugan et al [6], Medeiros et al [7][8] and Gongyan et al [9]. These authors presented methods that implement the particle filter described in [1] on technologies exploiting parallelism.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A tracking algorithm suitable for embedded systems implementation

Farah

Gan

Langlois

et al. 2011

2011 18th IEEE International Conference on Electronics, Circuits, and Systems

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Particle filters have been widely used for video tracking due to their robustness. However, most particle filter algorithm implementations are computationally expensive which makes them ill-suited for real-time embedded systems. There have been some attempts to provide hardware implementations for the particle filter, but none of them tried to simplify the algorithm first in order to make it more efficient for the hardware implementation. In this paper, a new sampling algorithm inspired from the particle filter methodology is proposed. It includes a resampling scheme that uses a new method to assign the number of particles between filter iterations and a criterion to reduce the number of processed samples, both in order to reduce the computational burden. Our experiments demonstrate that the algorithm can be as accurate as the CONDENSATION algorithm, while reducing the computational load by a factor of 30%.I.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Implementation and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A tracking algorithm suitable for embedded systems implementation

Farah

Gan

Langlois

et al. 2011

2011 18th IEEE International Conference on Electronics, Circuits, and Systems

View full text Add to dashboard Cite

show abstract

“…Mixed mode implementations -that is, partiallyanalog and partially-digital realizations -have also been explored. In such mixed-mode approaches, the analog components are used for the non-linear computations that are involved in particle filtering [19]. In design efforts towards computer vision applications, in [14] a shared memory multiprocessor implementation of a particle-filter-based 3D facial pose tracking algorithm was developed which was shown to provide significant performance gains.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of embedded computer vision systems based on particle filters

Saha

Bambha

Bhattacharyya

2010

Computer Vision and Image Understanding

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a b s t r a c tParticle filtering methods are gradually attaining significant importance in a variety of embedded computer vision applications. For example, in smart camera systems, object tracking is a very important application and particle filter based tracking algorithms have shown promising results with robust tracking performance. However, most particle filters involve vast amount of computational complexity, thereby intensifying the challenges faced in their real-time, embedded implementation. Many of these applications share common characteristics, and the same system design can be reused by identifying and varying key system parameters and varying them appropriately. In this paper, we present a System-on-Chip (SoC) architecture involving both hardware and software components for a class of particle filters. The framework uses parameterization to enable fast and efficient reuse of the architecture with minimal re-design effort for a wide range of particle filtering applications as well as implementation platforms.Using this framework, we explore different design options for implementing three different particle filtering applications on field-programmable gate arrays (FPGAs). The first two applications involve particle filters with one-dimensional state transition models, and are used to demonstrate the key features of the framework. The main focus of this paper is on design methodology for hardware/software implementation of multi-dimensional particle filter application and we explore this in the third application which is a 3D facial pose tracking system for videos. In this multi-dimensional particle filtering application, we extend our proposed architecture with models for hardware/software co-design so that limited hardware resources can be utilized most effectively. Our experiments demonstrate that the framework is easy and intuitive to use, while providing for efficient design and implementation. We present different memory management schemes along with results on trade-offs between area (FPGA resource requirement) and execution speed.

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“…Mixed mode implementations -that is, partially-analog and partially-digital realizations -have also been explored. In such mixed-mode approaches, the analog components are used for the non-linear computations that are involved in particle filtering [75]. Hendeby et al explore the use of graphics processing units to implement a parallel particle filtering system [34].…”

Section: Parameterized Design Framework For Particle Filter Systemsmentioning

confidence: 99%

Design Methodology for Embedded Computer Vision Systems

Bhattacharyya

Saha

2009

Embedded Computer Vision

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Computer vision has emerged as one of the most popular domains of embedded applications. Though various new powerful embedded platforms to support such applications have emerged in recent years, there is a distinct lack of efficient domain-specific synthesis techniques for optimized implementation of such systems. In this thesis, four different aspects that contribute to efficient design and synthesis of such systems are explored:(1) Graph Transformations: Dataflow modeling is widely used in digital signal processing (DSP) systems. However, support for dynamic behavior in such systems exists mainly at the modeling level and there is a lack of optimized synthesis techniques for these models. New transformation techniques for efficient system-on-chip (SoC) design methods are proposed and implemented for cyclo-static dataflow and its parameterized version (parameterized cyclo-static dataflow) -two powerful models that allow dynamic reconfigurability and phased behavior in DSP systems.

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Mixed-mode Implementation of Particle Filters

Cited by 5 publications

References 12 publications

A tracking algorithm suitable for embedded systems implementation

A tracking algorithm suitable for embedded systems implementation

Design and implementation of embedded computer vision systems based on particle filters

Design Methodology for Embedded Computer Vision Systems

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