Using programmable system-on-chip to implement computer vision functions poses many challenges due to highly constrained resources in cost, size and power consumption. In this work, we propose a new neuro-inspired image processing model and implemented it on a system-on-chip Xilinx Z702c board. With the attractor neural network model to store the object’s contour information, we eliminate the computationally expensive steps in the curve evolution re-initialisation at every new iteration or frame. Our experimental results demonstrate that this integrated approach achieves accurate and robust object tracking, when they are partially or completely occluded in the scenes. Importantly, the system is able to process 640 by 480 videos in real-time stream with 30 frames per second using only one low-power Xilinx Zynq-7000 system-on-chip platform. This proof-of-concept work has demonstrated the advantage of incorporating neuro-inspired features in solving image processing problems during occlusion.
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机译：由于成本，尺寸和功耗方面的资源严重受限，因此使用可编程的片上系统来实现计算机视觉功能会带来许多挑战。在这项工作中，我们提出了一种新的，受神经启发的图像处理模型，并在片上系统Xilinx Z702c板上实现了该模型。利用吸引子神经网络模型来存储对象的轮廓信息，我们消除了在每次新的迭代或帧时重新进行曲线演化的计算步骤。我们的实验结果表明，当部分或完全遮挡场景时，这种集成方法可以实现准确而强大的对象跟踪。重要的是，该系统仅使用一个低功耗Xilinx Zynq-7000片上系统平台就能够以每秒30帧的速度实时处理640 x 480个视频。这项概念验证工作已经证明了在解决遮挡过程中图像处理问题时融入神经启发特征的优势。
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