Hardware/software co-design of particle filter and its application in object tracking

Li, Shih‐An; Hsu, Chen-Chien; Lin, Wen-Ling; Wang, Jui-Pin

doi:10.1109/icsse.2011.5961879

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…However, few implementations [6] [8] have been using off-chip memory access which is costly. Additionally, most implementations [2][5] [9] are restricted to gray scale image with limited image size.…”

Section: Related Workmentioning

confidence: 99%

“…It also focused on memory management schemes for three different applications achieving 4.6 fps to 27 fps. Hardware/software co-design has been proposed in [6], where weight of particles is calculated using software on NIOS-II processor, while particle update is done using hardware circuit. SSRAM/SDRAM is used for storing the image data after reading from CMOS sensor.…”

Section: Related Workmentioning

confidence: 99%

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Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video

Velmurugan

Patkar

2015

2015 28th International Conference on VLSI Design

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Real-time particle filter based object tracking in videos on embedded platforms (FPGA) is challenging because of its resource usage and computational complexity. Furthermore, minor changes to the algorithm will need changes in the hardware. To address these issues, we propose a parametrizable FPGA framework for particle filter based object tracking algorithm. This parametrizable implementation can be used for various image sequences, object sizes and number of particles. By changing few parameters, this parametrization leads to appropriate changes in hardware resources resulting in efficient real-time operation of the algorithm. Experimental results show better tracking from the implementation and the proposed architecture can run particle filter algorithm for a color video sequence with 650 fps on average.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video

Velmurugan

Patkar

2015

2015 28th International Conference on VLSI Design

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“…It is important to develop a real time working algorithm though it is a challenging task. In recent years real-time working systems [7]- [12] and a number of tracking algorithms [1]- [6] have been developed. Particle filter [2], [6], [8], [13] is a powerful method to localize target, it gives high-precision results in complex situations, where as it is computationally expensive so particle filter is not suitable for real time Implementation and it is highly sensitive to noise too.…”

Section: Introductionmentioning

confidence: 99%

Real Time Implementation of Object Tracking Through Webcam

Cherian¹

2014

IJRET

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Real time object detection and tracking is an important task in various computer vision applications. For robust object tracking the factors like object shape variation, partial and full occlusion, scene illumination variation will create significant problems. We introduce object detection and tracking approach that combines Prewitt edge detection and kalman filter. The target object's representation and the location prediction are the two major aspects for object tracking this can be achieved by using these algorithms. Here real time object tracking is developed through webcam. Experiments show that our tracking algorithm can track moving object efficiently under object deformation, occlusion and can track multiple objects.

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“…(5) is the importance sampling distribution and (6) are the importance weights. The posterior distribution is then approximated by a finite set of particles using the expression in (7).…”

Section: Importance Samplingmentioning

confidence: 99%

“…However, there are not only hardware approaches, but also software/hardware mixed implementations. For instance, in [7] a SoPC (System on Programmable Chip) model with hardware accelerators is proposed, targeting an Altera Cyclone II FPGA. An embedded processor is in charge of computing weight values, whereas the particle update is carried out in hardware, since it is a highly repetitive process.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

Rodríguez

Moreno

2015

IEEE Trans. Comput.

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Abstract-Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.

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Hardware/software co-design of particle filter and its application in object tracking

Cited by 11 publications

References 21 publications

Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video

Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video

Real Time Implementation of Object Tracking Through Webcam

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

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