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to the corresponding sequentiel algorithms. Further-
more, as resampling is applied at every iteration of
the proposed algorithm, this can evaluating previous
particle weights. The final goal of this parallel imple-
mentation is to develop algorithms and architectures
that can reach the minimum execution time.
6 CONCLUSIONS
In this work, we have designed and implemented
a preliminary real time particle filter algorithm that
makes use of our MP-SOC architecture (HNCP) to
execute the algorithms main performance bottleneck.
Our strategy uses specific communication functions
based on our Parallel skeleton library for reducing
the computational efforts generated by the sequential
evaluation (Particle weights computation). Our ex-
periments on different video sequences showed that
search of Region of Interest (ROI) are accelerated
in order to achieve real-time tracking with relatively
small number of particles. The experiment results
show that the method can get a good effect and
speedup. This allows us to propose a particle filter
framwork for fast face tracking to achieve real time
performance using our HNCP architecture. As fu-
ture work, the research goal concerns the algorithm
implementation in a manner suitable for an FPGA-
based intelligent camera. In particular, we aim to
develop a robust self-localization approach for mo-
bile robot equipped with our architecture of a smart
camera based on Xilinx FPGA (camera with sensors
for high resolution image acquisition equipped with
HNCP architecture). Therefore, it seems important
to find a properly way to report results of applying
particle filters to providing a mobile robot with au-
tonomous capabilities.
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FAST PROTOTYPING OF EMBEDDED IMAGE PROCESSING APPLICATION ON HOMOGENOUS SYSTEM - A
Parallel Particle Filter Tracking Method on Homogeneous Network of Communicating Processors (HNCP)
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