基于图像处理和特征提取算法的智能交通视频检索模型

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI:10.14569/ijacsa.2023.01406143
Xiaomin Zhao, Xinxin Wang
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引用次数: 0

摘要

智能交通是将数据驱动的信息与交通管理相结合,实现智能监控和检索功能的系统。为了进一步提高系统模型的检索精度,设计了一种新的检索模型。总结了系统的功能需求,详细分析了数据预处理、特征匹配、特征提取三个阶段。本研究采用均衡化、归一化等预处理措施,尽量减少噪声和亮度的负面影响。综合各种算法的性能,选择距离方法作为特征匹配方法,该方法适用性更广,更适合处理批量数据。其次,利用欧氏距离法提取关键帧,并将特征提取分为颜色、形状和纹理三部分。采用颜色矩、canny算子、灰度共生矩阵等方法对其进行提取,最终实现相关图像的检索。本研究对模型的检索性能进行了多次实验,并对单个特征和混合特征的检索结果进行了分析。实验结果表明,该算法在混合特征提取中表现较好。与单个特征的平均值相比,三个混合特征的查全率和查准率分别提高了13.78%和15.64%。此外,在大量并发特征的情况下,该算法也能满足基本要求。当并发数为100时,算法的平均响应时间为4.46秒。因此,研究所提出的算法有效地提高了视频检索的能力,能够满足时效性的要求,在实际应用中具有广泛的应用价值。Keywords-Matching提取;特征融合;图像检索;智能交通
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Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm
Intelligent transportation is a system that combines data-driven information with traffic management to achieve intelligent monitoring and retrieval functions. In order to further improve the retrieval accuracy of the system model, a new retrieval model was designed. The functional requirements of the system were summarized, and the three stages of data preprocessing, feature matching, and feature extraction were analyzed in detail. The study adopted preprocessing measures such as equalization and normalization to minimize the negative effects of noise and brightness. Based on the performance of various algorithms, the distance method was selected as the feature matching method, which has a wider applicability and is better at processing bulk data. Next, the study utilizes Euclidean distance method to extract keyframes and divides the feature extraction into three parts: color, shape, and texture. The methods of color moment, canny operator, and grayscale cooccurrence matrix are used to extract them, and ultimately achieve relevant image retrieval. The research conducted multiple experiments on the retrieval performance of the model, and analyzed the results of retrieving single and mixed features. The experimental results showed that the algorithm performed better in the face of mixed feature extraction. Compared with the average value of a single feature, the recall and precision of the three mixed features increased by 13.78% and 15.64%, respectively. Moreover, in the case of a large number of concurrent features, the algorithm also met the basic requirements. When the concurrent number was 100, the average response time of the algorithm is 4.46 seconds. Therefore, the algorithm proposed by the research institute effectively improves the ability of video retrieval and can meet the requirements of timeliness, which can be widely applied in practical applications. Keywords—Matching extraction; feature fusion; image retrieval; intelligent transportation
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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