Abstract
Aiming at vision applications of our amphibious spherical robot, a real-time detection and tracking system adopting Gaussian background model and compressive tracking algorithm was designed and implemented in this article. Considering the narrow load space, the limited power resource and the specialized application scenarios of the robot, a heterogeneous computing architecture combining advanced Reduced Instruction-Set Computer (RISC) machine and field programmable gate array was proposed on the basis of Zynq-7000 system on chip.Under the architecture, main parts of the vision algorithms were implemented as software programs running on the advanced RISC machine-Linux subsystem. And customized image accelerators were deployed on the field programmable gate array subsystem to speed up the time-consuming processes of visual algorithms. Moreover, dynamic reconfiguration was used to switch accelerators online for reducing resource consumption and improving system adaptability. The word length of accelerators was optimized with simulated annealing algorithm to make a compromise between calculation accuracy and resource consumption. Experimental results confirmed the feasibility of the proposed architecture. The single board tracking system was able to provide an image processing rate of up to 89.2 frames per second at the resolution of 320 × 240, which could meet future demands of our robot in biological monitoring and multi-target tracking.
Highlights
Visual tracking is an active research topic in the field of computer vision with robotic applications ranging from visual servoing, automatic navigation and robot–human interaction
To verify the validation of the proposed heterogeneous system, an MYIRZ-Turn core board carrying Zynq-7000 System on chip (SoC) (XC7Z020) and an OV7670 camera was adopted to implement the detection and tracking system elaborated in the sections ‘Zynq-7000 SoC-based low-power real-time tracking system’ and ‘Optimization design of the proposed visual tracking system’
The image sequences were stored in the file system of the proposed system and were read by the implemented visual algorithms
Summary
Visual tracking is an active research topic in the field of computer vision with robotic applications ranging from visual servoing, automatic navigation and robot–human interaction. Given the initial state (e.g., position and scale) of a specific target in the first frame of a video or an image sequence, a visual tracker seeks to estimate the states of the target in the subsequent frames. Numerous tracking algorithms have been proposed,[5] it still remains a very challenging task to design a low-power real-time tracking system for mobile robotic applications.[6,7,8] On the one hand, most embedded processors for mobile robotic applications. Have a relatively weaker computational ability compared with multicore central processing unit (CPU) in workstations, which leads to difficulties for real-time image processing. Visual tracking algorithms have to process images successively, which covers processes of image preprocessing, appearance modelling, motion estimating, target locating and model updating.[5] it results in a great number of compute-extensive operations
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