【摘要】：車輛是城市道路交通最重要的組成部分,因此開展車輛視頻檢測器的研究對城市智能交通系統(tǒng)的發(fā)展具有重要的意義。隨著智能交通系統(tǒng)對產(chǎn)品小型化、低功耗、低成本且穩(wěn)定可靠的需求日益強烈,開展嵌入式車輛視頻檢測器的研究更具有了重要的現(xiàn)實意義和應(yīng)用價值。本論文基于TI公司的數(shù)字信號處理器(DSP)TMS320DM642平臺,對嵌入式車輛視頻檢測算法進行了研究,設(shè)計與實現(xiàn)了嵌入式的視頻車輛檢測器。論文的主要內(nèi)容和研究成果包括： 1.嵌入式算法設(shè)計：本文在虛擬線圈算法框架下,引入速度、方差和均值等計算簡單的統(tǒng)計量,提出了基于虛擬線圈統(tǒng)計量的快速視頻車輛檢測算法。該算法首先基于塊匹配原理計算虛擬線圈速度統(tǒng)計量,然后采用高階統(tǒng)計量分塊法來進行背景建模并得到前景圖像的均值和方差統(tǒng)計量,最后通過統(tǒng)計量的邏輯運算來判斷車輛到達、經(jīng)過、離開、駐停等狀態(tài)。該算法在滿足實時性要求的前提下,對于光照、陰影等外界條件的干擾有較強的魯棒性,適合在嵌入式平臺上運行。實驗結(jié)果表明,該算法在PC機上可以達到50幀/秒的檢測速度,同時在理想條件下白天的檢測率可以達到98%以上,夜間的檢測率達到93%,在白天車輛密集且有陰影時檢測率達到94%,滿足實際使用需求。 2.程序?qū)崿F(xiàn)與優(yōu)化：本文以基于虛擬線圈統(tǒng)計量的車輛檢測算法為軟件核心,在DM642平臺上實現(xiàn)了嵌入式視頻車輛檢測器。通過使用內(nèi)聯(lián)函數(shù)、展開循環(huán)、重排軟件流水、利用乒乓機制傳輸數(shù)據(jù)等多種手段對程序進行了優(yōu)化。實際道路的測試結(jié)果表明,本文研制的嵌入式車輛檢測器能夠達到20幀/秒的檢測速度,達到了實時檢測,同時車輛檢測率白天可以達到96%,夜晚達到90%,誤檢率白天不高于2%,漏檢率白天為4%,誤檢率夜晚不高于4%,漏檢率夜晚為10%。 測試結(jié)果表明,本文研制的基于DM642的視頻車輛檢測器能夠?qū)囕v進行實時的檢測,同時也保證了檢測的準確性,在檢測速度和檢測率兩者之間的平衡性上表現(xiàn)出色,為以后嵌入式視頻車輛檢測器的研制與應(yīng)用奠定了一定的基礎(chǔ)。
[Abstract]:Vehicle is the most important part of urban road traffic, so the research of vehicle video detector is of great significance to the development of urban intelligent transportation system. With the increasing demand of intelligent transportation system for miniaturization, low power consumption, low cost, stability and reliability, the research of embedded vehicle video detector has important practical significance and application value. In this paper, based on the digital signal processor (DSP) TMS320DM642 platform of TI Company, the embedded vehicle video detection algorithm is studied, and the embedded video vehicle detector is designed and implemented. The main contents and research results of the paper include: 1. Embedded algorithm design: in the framework of virtual coil algorithm, this paper introduces simple statistics such as speed, variance and mean, and proposes a fast video vehicle detection algorithm based on virtual coil statistics. Firstly, the virtual coil velocity statistics are calculated based on the block matching principle, and then the background modeling is carried out by using the high-order statistics block method, and the mean and variance statistics of the foreground image are obtained. Finally, the logical operation of statistics is used to judge the state of vehicle arrival, passing, leaving, stopping and so on. On the premise of meeting the real-time requirements, the algorithm has strong robustness to the interference of light, shadow and other external conditions, and is suitable for running on embedded platforms. The experimental results show that the detection speed of the algorithm can reach 50 frames per second on PC, and the detection rate can reach 98% in daytime and 93% in night under ideal conditions. When the vehicles are dense and shaded during the day, the detection rate reaches 94%, which meets the actual demand. 2. Program implementation and optimization: this paper takes the vehicle detection algorithm based on virtual coil statistics as the software core, and implements the embedded video vehicle detector on DM642 platform. The program is optimized by using inline function, expanding loop, rearranging software pipeline, using ping-pong mechanism to transmit data and so on. The test results of the actual road show that the embedded vehicle detector developed in this paper can achieve the detection speed of 20 frames per second and achieve real-time detection. At the same time, the vehicle detection rate can reach 96% during the day and 90% at night. The false detection rate is not more than 2% during the day, the missed detection rate is 4% during the day, the false detection rate is not more than 4% at night, and the missed detection rate is 10% at night. The test results show that the video vehicle detector based on DM642 can detect the vehicle in real time, at the same time, it also ensures the accuracy of the detection, and performs well in the balance between the detection speed and the detection rate. It lays a certain foundation for the development and application of embedded video vehicle detector in the future.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：U495;TP368.1

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本文編號：2481928