本文關(guān)鍵詞：智能交通中行人檢測算法的研究與實現(xiàn)　出處：《大連海事大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 智能交通系統(tǒng) 行人檢測 Adaboost DM642

【摘要】：在智能交通領(lǐng)域中,行人的檢測是一個重要且基本的任務(wù),由于檢測場景的復(fù)雜性,以及行人本身形態(tài)、姿態(tài)的復(fù)雜多變性,使得行人檢測,特別是基于嵌入式設(shè)備的行人檢測一直是計算機(jī)視覺研究中頗具難度的問題。隨著智能交通系統(tǒng)的發(fā)展,行人檢測系統(tǒng)有著更廣闊的應(yīng)用前景,但同時也對系統(tǒng)的體積、功耗、成本及穩(wěn)定性提出更高的要求,研究基于嵌入式的行人檢測系統(tǒng)具有重大的實用價值。本文設(shè)計實現(xiàn)一個基于Adaboost學(xué)習(xí)算法并且在TMS320DM642平臺上實現(xiàn)的行人檢測系統(tǒng)。論文的主要內(nèi)容和研究成果包括： 1.根據(jù)對行人檢測算法的相關(guān)理論的學(xué)習(xí),選擇了可移植到TSM320DM642平臺上的基于Adaboost的行人檢測算法,本文設(shè)計的系統(tǒng)使用基本的Haar-like矩形特征以及專門應(yīng)用于行人檢測三角特征,應(yīng)用積分圖像的方法計算特征值。利用OpenCV實現(xiàn)了基于Adaboost的行人檢測算法的仿真,訓(xùn)練得到級聯(lián)分類器,在PC機(jī)上進(jìn)行了多組實驗,取得良好的檢測效果。 2.根據(jù)設(shè)計需要,對硬件系統(tǒng)進(jìn)行選擇規(guī)劃,選擇TI公司的TMS320DM642芯片作為主控芯片,同時對TMS320DM642視頻口的配置進(jìn)行細(xì)致的研究學(xué)習(xí),選擇視頻采集輸出芯片,并根據(jù)PCB設(shè)計原則設(shè)計視頻版的PCB板。 3.利用集成實時操作系統(tǒng)DSP/BIOS進(jìn)行軟件開發(fā),在RF5框架下,將系統(tǒng)分為圖像采集、圖像處理和圖像顯示三個部分。并將基于Adaboost的行人檢測算法移植到DM642平臺上,利用CCS提供的各類調(diào)試工具對代碼進(jìn)行分析優(yōu)化,通過實驗測試表明基于DM642的行人檢測系統(tǒng)每秒可檢測10幀圖像,基本滿足實時性要求。
[Abstract]:Pedestrian detection is an important and basic task in the field of intelligent transportation. Due to the complexity of detection scene and the complex variability of pedestrian morphology and posture pedestrian detection makes pedestrian detection. In particular, pedestrian detection based on embedded devices has been a difficult problem in computer vision research. With the development of intelligent transportation system, pedestrian detection system has a broader application prospects. But at the same time, the volume, power consumption, cost and stability of the system are also required. It is of great practical value to study the pedestrian detection system based on embedded system. This paper designs and implements a learning algorithm based on Adaboost and realizes pedestrian detection on TMS320DM642 platform. The main contents and research results include:. 1. According to the theory of pedestrian detection algorithm, we choose the pedestrian detection algorithm based on Adaboost which can be transplanted to TSM320DM642 platform. The system designed in this paper uses the basic Haar-like rectangular feature and is specially applied to the pedestrian detection triangle feature. The method of integral image is used to calculate the eigenvalue. The simulation of pedestrian detection algorithm based on Adaboost is realized by using OpenCV. The cascade classifier is trained and many experiments are carried out on PC. Good results were obtained. 2. According to the design needs, the hardware system is selected and the TMS320DM642 chip of TI company is chosen as the main control chip. At the same time, the configuration of TMS320DM642 video port is studied carefully, the video capture and output chip is selected, and the PCB board of video version is designed according to the design principle of PCB. 3. The software is developed by using DSP/BIOS, which is an integrated real-time operating system. The system is divided into image collection under the framework of RF5. Image processing and image display are three parts. The pedestrian detection algorithm based on Adaboost is transplanted to DM642 platform, and the code is analyzed and optimized by various debugging tools provided by CCS. The experimental results show that the pedestrian detection system based on DM642 can detect 10 frames of images per second, which basically meets the real-time requirements.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;U491.226;TP391.41

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本文編號：1420839