Reconfigurable Shape-Adaptive Template Matching Architectures
FCCM '02 Proceedings of the 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Architectures for efficient implementation of particle filters
Architectures for efficient implementation of particle filters
Algorithmic and Architectural Design Methodology for Particle Filters in Hardware
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Model-Based OpenMP Implementation of a 3D Facial Pose Tracking System
ICPPW '06 Proceedings of the 2006 International Conference Workshops on Parallel Processing
2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints
Journal of Intelligent and Robotic Systems
Learning for multi-view 3d tracking in the context of particle filters
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
3D facial pose tracking in uncalibrated videos
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering
IEEE Transactions on Image Processing
Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters
Journal of Signal Processing Systems
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Particle 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.