Abstract
This paper presents a study on the optimization of the tracking system designed for patients with Parkinson’s disease tested at a day hospital center. The work performed significantly improves the efficiency of the computer vision based system in terms of energy consumption and hardware requirements. More specifically, it optimizes the performances of the background subtraction by segmenting every frame previously characterized by a Gaussian mixture model (GMM). This module is the most demanding part in terms of computation resources, and therefore, this paper proposes a method for its implementation by means of a low-cost development board based on Zynq XC7Z020 SoC (system on chip). The platform used is the ZedBoard, which combines an ARM Processor unit and a FPGA. It achieves real-time performance and low power consumption while performing the target request accurately. The results and achievements of this study, validated in real medical settings, are discussed and analyzed within.
Highlights
Nowadays human pedestrian tracking and object detection systems are taking on an important role in real life applications
Based on the latest advances, with special mention to the work of Bulat et al [5] which compared three tracking algorithms focusing on the advantages of the use of FPGAs over GPU and CPU, the FPGA-SoC is a suitable choice for the base architecture to run and to accelerate the algorithm, and to meet the required timings and respecting the privacy needs of Parkinson’s patients by gathering only the tracking information, without storing images or video data
The algorithms chosen for HW acceleration were mixture of Gaussian (MoG) because of its stability for background subtraction, and a Kalman filter for the tracking because of its robustness based on a statistical model
Summary
Nowadays human pedestrian tracking and object detection systems are taking on an important role in real life applications. These systems attract considerable interest in the industrial and scientific communities, and a lot of methods have been proposed and developed. New customizable hardware (Hw) solutions, operating systems (OS) and software (Sw) applications have already been developed for research purposes, concluding in robust prototypes that have derived in ready-to-market solutions These systems are integrated in a single chip and embedded on a board. A statistic model is built to cover the static elements in the scene, and those elements that do not fit into that distribution will be considered regions of interest (ROIs) Those ROIs that correspond to moving objects will provide the initial step towards defining the pedestrian trajectories.
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