| 改进光流法和GMM融合的车辆实时检测算法研究 |
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| 引用本文： | 陈立潮,解丹,曹建芳,张睿.改进光流法和GMM融合的车辆实时检测算法研究[J].智能系统学报,2021,16(2):271-278. |
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| 作者姓名： | 陈立潮 解丹 曹建芳 张睿 |
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| 作者单位： | 1. 太原科技大学 计算机科学与技术学院，山西 太原 030024;2. 忻州师范学院 计算机科学与技术系，山西 忻州 034000 |
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| 摘 要： | 针对传统光流算法受光照影响较大和在不同场景中检测效果差别较大等问题，提出一种改进的光流法与混合高斯背景模型相融合的运动车辆实时检测算法（improved optical flow and gaussian mixture model，IOFGMM）。首先，在光流算法中加入限制条件使得不同梯度点处采用不同约束；其次，融合高斯混合背景模型（gaussian mixture model，GMM）；最后，采用提出的融合算法比较目标框的数量和目标框之间的重叠面积，从而在监控视频中显示出融合后的车辆检测信息。实验结果表明:该算法在3种不同场景视频上的检测效果达到了84.80%的平均准确率，84.79%的平均召回率以及84.63%的平均F1值。与经典的光流法和高斯混合背景模型及基于这两种理论的算法相比，IOFGMM算法的各项指标平均有37%的提高，具有良好的检测效果。
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| 关 键 词： | IOFGMM检测算法 光流法 高斯混合背景模型 信息融合 实时检测 梯度 光照 约束 |
Research on vehicle real-time detection algorithm based on improved optical flow method and GMM |
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| CHEN Lichao,XIE Dan,CAO Jianfang,ZHANG Rui.Research on vehicle real-time detection algorithm based on improved optical flow method and GMM[J].CAAL Transactions on Intelligent Systems,2021,16(2):271-278. |
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| Authors: | CHEN Lichao XIE Dan CAO Jianfang ZHANG Rui |
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| Affiliation: | 1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;2. Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China |
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| Abstract: | To solve the problem of the optical flow algorithm being greatly affected by illumination and the highly variable detection effect in different scenes, in this paper, we propose an improved optical flow and Gaussian mixture model (IOFGMM) algorithm for the real-time detection of moving vehicles. First, a restriction is added to the optical flow algorithm whereby different constraints are used at different points. Then, a Gaussian mixture model (GMM) is fused. Finally, the number of target boxes and the area in which the target boxes overlap are compared by the proposed fusion algorithm. The vehicle detection information after fusion is displayed in the surveillance video. Experimental results show that the detection performance of the IOFGMM algorithm achieved an average accuracy rate of 84.80%, an average recall rate of 84.79%, and an average F1 value of 84.63% for videos of three different scenes. Compared with the classical optical flow method, the GMM, and the algorithm based on these two theories, the IOFGMM algorithm shows an average improvement of 37% in each metric. Therefore, we can conclude that the IOFGMM algorithm has good detection performance. |
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| Keywords: | IOFGMM detection algorithm optical flow method gaussian mixture background model Information fusion real-time detection gradient illumination constraint |
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