Proposed hardware architectures of particle filter for object tracking

Abstract

In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video

“…al. [9] proposed an effective hardware architecture of sample important re-sample particle filter (SIRF) for gray scale intensity images using various engines like re-sampling engine, weight calculation engine, output engine and particle generator along with dual port FIFO. The FIFO is used for storing particle state vector consisting of gray level intensity, x-y positions and the velocity in both (xy) directions.…”

“…As shown in (9), this calculation requires division operation, which is implemented using CORDIC function and utilizes DSP multiplier resources available on FPGA. This division of 40 bit number by 30 bit number is developed using algorithm mentioned in [13], This uses comparison and subtraction for division, which is found to be satisfactory for our implementation.…”

“…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. The parameterizable framework discussed in [5], is based on hardware/software approach works reasonably for gray scale image sequences.…”

Estimating the number of competing terminals without a state variation detector in wireless LAN

“…According to this system model, we can estimate the time-varying state n k sequentially based on the observed measurement ξ k by dynamic filters, e.g., the Kalman filter, particle filter, and their variants. In general cases, i.e., apart from the linear and Gaussian case, there exist a number of sub-optimal approaches such as particle filtering (PF) [12,13]. The Kalman filter also can be applied as a sub-optimal approach to non-linear model by extending it using Taylor series (in this case, still Gaussian noise is assumed) [14].…”

Visual Object Target Tracking Using Particle Filter: A Survey

“…Proposed hardware architectures of particle filter for object tracking [82] In this paper, authors proposed three different architectures for Sequential Importance Resampling Filter (SIRF) implementation, in which the results shown the resource reduction and speed up advantages of the architectures The authors focused mostly on reducing the execution time.…”