Authors:
Hanen Chenini
;
Jean Pierre Derutin
and
Thierry Chateau
Affiliation:
Blaise Pascal University, France
Keyword(s):
Face Tracking, K-Nearest Neighbor (KNN), Parallel Implementations, Homogeneous Network of Communicating Processors.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
This article discusses the design of an application specific MP-SoC (Multi- Processors System on Chip) architecture dedicated to face tracking algorithm. The proposed algorithm tracks a Region-Of-Interest (ROI) by determining the similarity measures between the reference and the target frames. In our approach, this measure is the estimation of the Kullback-Leibler divergence from the K-nearest neighbor (KNN) framework. The metric between pixels is an Euclidean norm in a joint geometric and radiometric space. The adopted measure allows us to check if the regions have similar colors and also if these colors appear at the same location. Considering the necessary computation amounts, we propose a parallel hardware implementation of the developed algorithm on MP-SoC architecture. Creating multiple processors in one system is hard for software developers using traditional hardware design approaches due to the complexity to design software models suitable for such FPGA implementations. In o
rder to deal with this problem, we have introduced a CubeGen tool to avoid fastidious manual editing operations for the designer. This new methodology enables us to instantiate a generic Homogeneous Network of Communicating Processors (called HNCP) tailored for our targeted application. Our implementations are demonstrated using the Xilinx FPGA chip XC6VLX240T.
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