基於Zynq SoC的實時嵌入式 行人追蹤系统;Real-time Embedded Human Tracking System in Zynq SoC

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題名:	基於Zynq SoC的實時嵌入式行人追蹤系统;Real-time Embedded Human Tracking System in Zynq SoC
作者:	吳柏賢;Wu, Po-Hsien
貢獻者:	電機工程學系
關鍵詞:	行人追蹤;物件偵測;深度學習;human follwing;deep learning;object detection
日期:	2022-06-08
上傳時間:	2022-07-14 01:09:04 (UTC+8)
出版者:	國立中央大學
摘要:	行人跟隨機器人一直是個在非常熱門的應用，隨著近年來深度學習的流行與其所需硬體設備的發展，基於深度學習的追蹤演算法被廣泛應用在跟隨機器人的應用上。越來越多基於圖像處理追蹤的方法被提出，其中也包含許多深度學習方法，這些方法，最依賴於資源強大的計算資源，如 GPU 服務器。本論文關注行人追蹤任務的複雜度問題，通過它提出一種更高效的行人追蹤方法，結合了單物件追蹤器KCF、行人偵測模組YOLO v3與相似度比對模組，以克服追蹤任務中計算速度和準確度的衝突。為了保留系統因應神經網路發展靈活更動的可行性，我們選擇透過基於Zynq SoC的HW/SW Co-design，PL(Programming Logic)端使用AXI總線協議來與PS(Processing System)端溝通，由PS端處理非神經網路運算與資料傳輸，PL端處理所有神經網路相關運算。此外，我們在Zynq UltraScale + MPSoC ZCU104中引入了一個新的AI加速器框架Vitis-AI及其深度學習單元（DPU）來加速系統中的YOLO v3行人偵測模組。最後我們的行人跟蹤方法在增加了一個單物件跟蹤器後，系統處理速度實現了1.27倍的加速。與CPU Intel Core i7700k@4.2GHz 上的系統相比，ZCU104 上的YOLO v3行人檢測模組速度加速了1.53 倍，而功耗節省了87.1%，在ZCU104上達到409 GOPs且只需耗能15.57W，達到0.29 GOPS/s/DSP的效能。整體系統能以11.5 FPS執行 ;Human following robots have been a very popular application, and with the recent popularity of deep learning and the development of the required hardware devices, deep learning based tracking algorithms are widely used in following robots applications. More and more tracking methods based on image processing have been proposed, which also include many deep learning methods that rely most on powerful computing resources such as GPU servers. This paper focuses on the complexity of human tracking task by which a more efficient human tracking method is proposed, combining single-object tracker KCF, human detection module YOLO v3 and similarity comparison module to overcome the conflict of computational speed and accuracy in tracking task. In order to preserve the feasibility of flexible system changes in response to neural network development, we chose to use the HW/SW co-design based on Zynq SoC, with the PL (Programming Logic) part using AXI bus protocol to communicate with the PS (Processing System) part, and the PS part handling non-neural network computations and data transfers. The PL part deal with all neural network related computations. In addition, we introduced a new AI accelerator framework, Vitis-AI, and its Deep Processing Unit (DPU) in Zynq UltraScale + MPSoC ZCU104 to accelerate the YOLO v3 human detection module in the system. Finally, our human tracking approach can run at 11.5 FPS, achieving a 1.27x acceleration in system processing speed with the addition of a single-object tracker. Compared to the system on the CPU Intel Core i7700k@4.2GHz, the YOLO v3 human detection module on the ZCU104 accelerates 1.53 times faster while saving 87.1% in power consumption, reaching 409 GOPs on the ZCU104 and consuming only 15.57W, achieving a performance of 0.29 GOPS/s/DSP.
顯示於類別:	[電機工程研究所] 博碩士論文

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