【摘要】：智能視頻監(jiān)控越來(lái)越多地運(yùn)用在社會(huì)各層人民的工作和生活中,其給人們帶來(lái)的便捷不言而喻。在智能交通系統(tǒng)中,對(duì)運(yùn)動(dòng)車輛的檢測(cè)和跟蹤是整個(gè)交通事件檢測(cè)和視頻監(jiān)控系統(tǒng)智能化的主要內(nèi)容和關(guān)鍵技術(shù)。運(yùn)動(dòng)車輛檢測(cè)的目的是將視頻序列中感興趣的區(qū)域無(wú)誤地提取出來(lái),這樣才能為后續(xù)的跟蹤提取相應(yīng)目標(biāo)的特征;運(yùn)動(dòng)車輛跟蹤的目的是需要實(shí)時(shí)了解其運(yùn)動(dòng)特征,包括行駛方向、速度、軌跡等。做好車輛檢測(cè)和跟蹤的工作為交通事件檢測(cè)、行為分析等奠定了堅(jiān)實(shí)的基礎(chǔ)。但是由于目標(biāo)檢測(cè)和跟蹤的場(chǎng)景復(fù)雜,不可預(yù)測(cè)因素增多,目標(biāo)因?yàn)閿z像頭的角度及距離等原因而大大增加了目標(biāo)跟蹤的難度。因此,開(kāi)展運(yùn)動(dòng)目標(biāo)檢測(cè)及跟蹤方法的研究具有重要意義。本文圍繞高速公路運(yùn)動(dòng)車輛檢測(cè)及跟蹤方法開(kāi)展了如下研究工作:(1)提出一種基于五幀差分和LOG算子相結(jié)合的運(yùn)動(dòng)車輛檢測(cè)方法。首先在幀差類方法中,分別對(duì)二幀差分、三幀差分、四幀差分、五幀差分進(jìn)行詳細(xì)研究和仿真,通過(guò)實(shí)驗(yàn)結(jié)果對(duì)比得出五幀差分檢測(cè)前景目標(biāo)的效果最好,然后結(jié)合能夠檢測(cè)運(yùn)動(dòng)目標(biāo)完整信息的LOG邊緣檢測(cè)算子進(jìn)行運(yùn)動(dòng)目標(biāo)檢測(cè)。實(shí)驗(yàn)結(jié)果表明,兩種算法結(jié)合的目標(biāo)檢測(cè)方法對(duì)環(huán)境的適應(yīng)能力強(qiáng),去噪效果良好,對(duì)目標(biāo)檢測(cè)信息完整,達(dá)到了方法改進(jìn)的效果。(2)結(jié)合正則粒子濾波改善Camshift算法的運(yùn)動(dòng)車輛跟蹤方法。在利用Camshift算法進(jìn)行目標(biāo)跟蹤的過(guò)程中,其主要是根據(jù)所跟蹤目標(biāo)的色調(diào)分布來(lái)進(jìn)行跟蹤的,因此能實(shí)現(xiàn)對(duì)具有與周圍環(huán)境相差較大的顏色的目標(biāo)進(jìn)行良好的跟蹤。但是容易受到目標(biāo)附近具有相似顏色的背景或環(huán)境干擾導(dǎo)致跟蹤丟失,為此,本文融入正則粒子濾波方法提出了一種改善Camshift的運(yùn)動(dòng)目標(biāo)跟蹤方法,該方法通過(guò)與運(yùn)動(dòng)目標(biāo)檢測(cè)方法相結(jié)合,實(shí)現(xiàn)對(duì)新目標(biāo)和相似目標(biāo)的良好處理,實(shí)現(xiàn)跟蹤的持續(xù)穩(wěn)定,能較好地實(shí)現(xiàn)監(jiān)控系統(tǒng)智能化。本文以高速公路運(yùn)動(dòng)車輛為研究對(duì)象,完成了基于幀差法和Camshift的運(yùn)動(dòng)車輛檢測(cè)和跟蹤方法的研究,豐富了監(jiān)控視頻中前景目標(biāo)檢測(cè)和跟蹤的研究?jī)?nèi)容,對(duì)其他場(chǎng)景的前景目標(biāo)檢測(cè)和跟蹤具有一定的借鑒意義。
[Abstract]:Intelligent video surveillance is more and more used in the work and life of people at all levels of society. In the intelligent transportation system, the detection and tracking of moving vehicles is the main content and key technology of the whole intelligent traffic incident detection and video surveillance system. The object of moving vehicle detection is to extract the region of interest from the video sequence unmistakably, so as to extract the features of the corresponding target for the subsequent tracking. The purpose of moving vehicle tracking is to know its motion characteristics in real time, including direction, speed, track and so on. The work of vehicle detection and tracking lays a solid foundation for traffic incident detection and behavior analysis. However, because the scene of target detection and tracking is complex and unpredictable factors increase, the target greatly increases the difficulty of target tracking because of the angle and distance of the camera. Therefore, it is of great significance to study the methods of moving target detection and tracking. The research work of this paper is as follows: (1) A moving vehicle detection method based on five-frame difference and LOG operator is proposed. First of all, in the frame difference class method, the two frame difference, three frame difference, four frame difference and five frame difference are studied and simulated in detail, and the results of experiment show that the effect of five-frame differential detection foreground target is the best. Then LOG edge detection operator which can detect the complete information of moving target is used to detect moving target. The experimental results show that the two algorithms have strong adaptability to the environment, good denoising effect, and complete target detection information. The improved method is achieved. (2) the moving vehicle tracking method based on Camshift algorithm is improved by using regular particle filter. In the process of target tracking using Camshift algorithm, it is mainly based on the hue distribution of the target being tracked, so it can achieve good tracking of the target with different colors from the surrounding environment. However, it is easy to lose tracking due to background or environmental interference with similar colors near the target. In this paper, a moving target tracking method to improve Camshift is proposed by incorporating regular particle filter. By combining with the moving target detection method, the new target and the similar target can be handled well, the tracking can be sustained and stable, and the monitoring system can be intelligentized. Based on frame difference method and Camshift, this paper studies the detection and tracking method of moving vehicle on expressway, which enriches the research content of foreground target detection and tracking in surveillance video. It can be used for reference for foreground target detection and tracking in other scenes.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;TP391.41

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本文編號(hào)：2417720