低複雜度的嵌入式物件偵測與追蹤系統設計;Low-Complexity Embedded Object Detection and Tracking System Design

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/73004

Title:	低複雜度的嵌入式物件偵測與追蹤系統設計;Low-Complexity Embedded Object Detection and Tracking System Design
Authors:	吳俊賢;Wu, Jun-Xian
Contributors:	資訊工程學系
Keywords:	物件偵測;物件追蹤;低複雜度;嵌入式;Object Detection;Object Tracking;Low-Complexity;Embedded
Date:	2017-01-18
Issue Date:	2017-05-05 17:38:16 (UTC+8)
Publisher:	國立中央大學
Abstract:	物聯網概念的興起，低複雜度的感測節點成為開發的趨勢。本論文為將完整的物件偵測和追蹤實作於低成本嵌入式DSP平台，並進行效能上的驗證，目的是開發出一個低複雜度、低硬體資源需求，且無作業系統的智慧型嵌入式攝影機系統。在移動物偵測方面，我們採用近似中值濾波法實作背景建模，由於其複雜度低，不需太多的記憶體資源，因此作為物件偵測的主要方法。接著我們應用PSO演算法，偵對移動物建立移動物樣板，並利用粒子群全域最佳化進行非線性的移動物追蹤。此系統在記憶體資源有限及開發成本有限的情況下，能達到即時運算的需求，且系統以C語言作為開發工具，可移植性高，未來可作為物聯網中智慧監控攝影機的應用。;In rising of IoT, low-complexity sensor node is the trend of development. This paper implements complete object detection and tracking on a low-cost DSP platform, and verify the system performance on efficacy. Our goal is to achieve an intelligent embedded camera of low-complexity, hardware-constrained, and without operating system. For detection, because of the low-complexity, the paper utilize Approximated Median Filter (AMF) to achieve background modeling for the main method of object detection. Then particle swarm optimization (PSO) is main method which is used as tracking strategy: First, build the target model for moving object. Through PSO algorithm, the system can track moving objects in the nonlinear system. Limited on the memory and development costs, the experiments and analysis still show the efficiency. Due to using C language as development tools, the system is high portability. The proposed system can be the IoT application system case in the future.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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