Design and FPGA Implementation of an Adaptive video Subsampling Algorithm for Energy-Efficient Single Object Tracking
Design and FPGA Implementation of an Adaptive video Subsampling Algorithm for Energy-Efficient Single Object Tracking
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DOI：
10.1109/icip40778.2020.9191146
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发表时间：
2020-10
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通讯作者：
Odrika Iqbal;Saquib Siddiqui;Joshua Martin;Sameeksha Katoch;A. Spanias;D. Bliss;Suren Jayasuriya
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作者：
Odrika Iqbal;Saquib Siddiqui;Joshua Martin;Sameeksha Katoch;A. Spanias;D. Bliss;Suren Jayasuriya
具有可编程区域的图像传感器（ROI）读数是一种新的敏感性技术，尤其是嵌入式计算机视觉，ROI可以在本文中执行单个对象跟踪的读数。与预测的Kalman过滤器相结合。一个新的领域硬件软件共同设计，用于嵌入式计算机视觉中的自适应视频子采样。
Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23 × improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision.